From 7c970b330cf9aeec0d7c6e46cf56f7bb7a27d5ec Mon Sep 17 00:00:00 2001
From: Yangge Li <li213@illinois.edu>
Date: Thu, 27 Oct 2022 17:31:49 -0500
Subject: [PATCH] change controller to decision_logic; add tutorial for agent
and map
---
demo/tacas2023/exp7/uncertain_agents.py | 12 +-
tutorial/Verse_Tutorial_Drone.ipynb | 75966 ++++++++++++++++++++-
tutorial/dl_sec4.py | 28 +
tutorial/dl_sec5.py | 61 +
tutorial/map_2lanes_lab.png | Bin 0 -> 144826 bytes
tutorial/test.py | 112 +-
tutorial/tutorial_agent.py | 33 +-
tutorial/tutorial_map.py | 29 +-
tutorial/tutorial_sensor.py | 32 +-
tutorial/tutorial_utils.py | 8 +-
verse/agents/base_agent.py | 2 +-
verse/agents/example_agent/car_agent.py | 2 +-
verse/agents/example_agent/sign_agent.py | 2 +-
verse/analysis/analysis_tree.py | 8 +-
verse/analysis/incremental.py | 6 +-
verse/automaton/guard.py | 28 +-
verse/automaton/reset.py | 10 +-
verse/parser/parser.py | 2 +-
verse/scenario/scenario.py | 62 +-
verse/sensor/base_sensor.py | 32 +-
20 files changed, 76092 insertions(+), 343 deletions(-)
create mode 100644 tutorial/dl_sec4.py
create mode 100644 tutorial/dl_sec5.py
create mode 100644 tutorial/map_2lanes_lab.png
diff --git a/demo/tacas2023/exp7/uncertain_agents.py b/demo/tacas2023/exp7/uncertain_agents.py
index cc15440b..4286b3cb 100644
--- a/demo/tacas2023/exp7/uncertain_agents.py
+++ b/demo/tacas2023/exp7/uncertain_agents.py
@@ -62,7 +62,7 @@ def computeD(exprs, symbol_x, symbol_w, x, w, x_hat, w_hat):
class Agent1(BaseAgent):
def __init__(self, id):
self.id = id
- self.controller = ControllerIR.empty()
+ self.decision_logic = ControllerIR.empty()
def dynamics(self, x, args):
w1, w2, dt = args
@@ -90,7 +90,7 @@ class Agent1(BaseAgent):
class Agent2(BaseAgent):
def __init__(self, id):
self.id = id
- self.controller = ControllerIR.empty()
+ self.decision_logic = ControllerIR.empty()
def dynamics(self, x, args):
w1, w2, dt = args
@@ -118,7 +118,7 @@ class Agent2(BaseAgent):
class Agent3(BaseAgent):
def __init__(self, id):
self.id = id
- self.controller = ControllerIR.empty()
+ self.decision_logic = ControllerIR.empty()
def dynamics(self, x, args):
w1, w2, dt = args
@@ -132,7 +132,7 @@ class Agent3(BaseAgent):
class Agent4(BaseAgent):
def __init__(self, id):
self.id = id
- self.controller = ControllerIR.empty()
+ self.decision_logic = ControllerIR.empty()
def dynamics(self, x, args):
w1, dt = args
@@ -164,7 +164,7 @@ class Agent5(BaseAgent):
def __init__(self, id):
# super().__init__(id, code, file_name)
self.id = id
- self.controller = ControllerIR.empty()
+ self.decision_logic = ControllerIR.empty()
self.init_cont = None
self.init_disc = None
self.static_parameters = None
@@ -213,7 +213,7 @@ class Agent6(BaseAgent):
def __init__(self, id):
# super().__init__(id, code, file_name)
self.id = id
- self.controller = ControllerIR.empty()
+ self.decision_logic = ControllerIR.empty()
self.init_cont = None
self.init_disc = None
self.static_parameters = None
diff --git a/tutorial/Verse_Tutorial_Drone.ipynb b/tutorial/Verse_Tutorial_Drone.ipynb
index 3960145b..92c223fb 100644
--- a/tutorial/Verse_Tutorial_Drone.ipynb
+++ b/tutorial/Verse_Tutorial_Drone.ipynb
@@ -35,19 +35,10 @@
},
{
"cell_type": "code",
- "execution_count": 1,
+ "execution_count": null,
"id": "d789ceb2",
"metadata": {},
- "outputs": [
- {
- "name": "stderr",
- "output_type": "stream",
- "text": [
- "`polytope` failed to import `cvxopt.glpk`.\n",
- "will use `scipy.optimize.linprog`\n"
- ]
- }
- ],
+ "outputs": [],
"source": [
"from tutorial_map import M3\n",
"\n",
@@ -73,7 +64,7 @@
},
{
"cell_type": "code",
- "execution_count": 2,
+ "execution_count": null,
"id": "0fbb7f1f",
"metadata": {},
"outputs": [],
@@ -99,7 +90,7 @@
},
{
"cell_type": "code",
- "execution_count": 3,
+ "execution_count": null,
"id": "7edf89dc",
"metadata": {},
"outputs": [],
@@ -128,7 +119,7 @@
},
{
"cell_type": "code",
- "execution_count": 4,
+ "execution_count": null,
"id": "ba6e3ba2",
"metadata": {},
"outputs": [],
@@ -153,7 +144,7 @@
},
{
"cell_type": "code",
- "execution_count": 5,
+ "execution_count": null,
"id": "61837729",
"metadata": {},
"outputs": [],
@@ -173,7 +164,7 @@
},
{
"cell_type": "code",
- "execution_count": 6,
+ "execution_count": null,
"id": "b046fe36",
"metadata": {},
"outputs": [],
@@ -192,7 +183,7 @@
},
{
"cell_type": "code",
- "execution_count": 7,
+ "execution_count": null,
"id": "6f54eada",
"metadata": {},
"outputs": [],
@@ -211,7 +202,7 @@
},
{
"cell_type": "code",
- "execution_count": 8,
+ "execution_count": null,
"id": "a1e2a90c",
"metadata": {},
"outputs": [],
@@ -237,20 +228,10 @@
},
{
"cell_type": "code",
- "execution_count": 9,
+ "execution_count": null,
"id": "cc1cdb50",
"metadata": {},
- "outputs": [
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "[[0.04333064463648151, 0.2696066563083741, 0.35135316205944433, 0.0, 0.0, 0.0]]\n",
- "{'drone1': ['Normal', 'T0']}\n",
- "{'drone1': ('AvoidUp', 'TAvoidUp')}\n"
- ]
- }
- ],
+ "outputs": [],
"source": [
"traces_simu = scenario.simulate(60, 0.2)\n",
"traces_veri = scenario.verify(60, 0.2)"
@@ -266,30 +247,10 @@
},
{
"cell_type": "code",
- "execution_count": 10,
+ "execution_count": null,
"id": "c1d70fcf",
"metadata": {},
- "outputs": [
- {
- "name": "stderr",
- "output_type": "stream",
- "text": [
- "ERROR:root:<<Cannot triangulate; no input points\n",
- "ERROR:root:<<Cannot triangulate; no input points\n",
- "ERROR:root:<<Cannot triangulate; no input points\n"
- ]
- },
- {
- "data": {
- "image/png": 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",
- "text/plain": [
- "<PIL.Image.Image image mode=RGB size=1024x768>"
- ]
- },
- "metadata": {},
- "output_type": "display_data"
- }
- ],
+ "outputs": [],
"source": [
"from verse.plotter.plotter3D import *\n",
"import pyvista as pv\n",
@@ -339,7 +300,7 @@
},
{
"cell_type": "code",
- "execution_count": 11,
+ "execution_count": null,
"id": "a7ce4fd2",
"metadata": {},
"outputs": [],
@@ -352,7 +313,7 @@
},
{
"cell_type": "code",
- "execution_count": 12,
+ "execution_count": null,
"metadata": {},
"outputs": [],
"source": [
@@ -396,7 +357,7 @@
},
{
"cell_type": "code",
- "execution_count": 13,
+ "execution_count": null,
"id": "57d0747c",
"metadata": {},
"outputs": [],
@@ -451,7 +412,7 @@
},
{
"cell_type": "code",
- "execution_count": 14,
+ "execution_count": null,
"id": "2187c4b7",
"metadata": {},
"outputs": [],
@@ -482,7 +443,7 @@
},
{
"cell_type": "code",
- "execution_count": 15,
+ "execution_count": null,
"id": "a16287c3",
"metadata": {},
"outputs": [],
@@ -501,7 +462,7 @@
},
{
"cell_type": "code",
- "execution_count": 16,
+ "execution_count": null,
"id": "d39dbe69",
"metadata": {},
"outputs": [],
@@ -520,23 +481,10 @@
},
{
"cell_type": "code",
- "execution_count": 17,
+ "execution_count": null,
"id": "c48ad5ad",
"metadata": {},
- "outputs": [
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "[[1.5274444414877735, -0.014701645224999393, -0.075719016794775, 0.0, 0.0, 0.0], [19.879804928864168, 0.43227456766397154, -0.39851528894906707, 0.0, 0.0, 0.0]]\n",
- "{'drone1': ['Normal', 'T1'], 'drone2': ['Normal', 'T1']}\n",
- "{'drone1': ('MoveDown', 'M12'), 'drone2': ['Normal', 'T1']}\n",
- "{'drone1': ('MoveUp', 'M10'), 'drone2': ['Normal', 'T1']}\n",
- "{'drone1': ('Normal', 'T2'), 'drone2': ['Normal', 'T1']}\n",
- "{'drone1': ('Normal', 'T0'), 'drone2': ['Normal', 'T1']}\n"
- ]
- }
- ],
+ "outputs": [],
"source": [
"traces_simu = scenario.simulate(60, 0.2)\n",
"traces_veri = scenario.verify(60, 0.2)"
@@ -552,32 +500,10 @@
},
{
"cell_type": "code",
- "execution_count": 18,
+ "execution_count": null,
"id": "5d180128",
"metadata": {},
- "outputs": [
- {
- "name": "stderr",
- "output_type": "stream",
- "text": [
- "ERROR:root:<<Cannot triangulate; no input points\n",
- "ERROR:root:<<Cannot triangulate; no input points\n",
- "ERROR:root:<<Cannot triangulate; no input points\n",
- "ERROR:root:<<Cannot triangulate; no input points\n",
- "ERROR:root:<<Cannot triangulate; no input points\n"
- ]
- },
- {
- "data": {
- "image/png": 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",
- "text/plain": [
- "<PIL.Image.Image image mode=RGB size=1024x768>"
- ]
- },
- "metadata": {},
- "output_type": "display_data"
- }
- ],
+ "outputs": [],
"source": [
"from verse.plotter.plotter3D import *\n",
"import pyvista as pv\n",
@@ -603,7 +529,7 @@
},
{
"cell_type": "code",
- "execution_count": 19,
+ "execution_count": null,
"id": "9f137299",
"metadata": {},
"outputs": [],
@@ -643,12 +569,12 @@
"id": "ea4a9f2c",
"metadata": {},
"source": [
- "Verse allow checking safety conditions while performing simulation and verification. The safety conditions in verse can be specified using Python assert statements in the decision logic. With the <code>any</code> and <code>all</code> functions, the user can also define safety conditions between different agents. In this example, we are going to add two safety conditions: 1) the distance between two agents in all x,y,z direction should always be greater than or equal to 1.0m and 2) both drones should never enter region x\\in[40,50], y\\in[-5,5], z\\in[-10,-6]. The two safety asserts in the <code>decisionLogic</code> is shown below. "
+ "Verse allow checking safety conditions while performing simulation and verification. The safety conditions in verse can be specified using Python assert statements in the decision logic. With the <code>any</code> and <code>all</code> functions, the user can also define safety conditions between different agents. In this example, we are going to add two safety conditions: 1) the distance between two agents in all x,y,z direction should always be greater than or equal to 1.0m and 2) both drones should never enter region $40\\leq x\\leq 50$, $-5\\leq y\\leq 5$, $-10\\leq z\\leq -6$. The two safety asserts in the <code>decisionLogic</code> is shown below. "
]
},
{
"cell_type": "code",
- "execution_count": 20,
+ "execution_count": null,
"id": "7a5d64cb",
"metadata": {},
"outputs": [],
@@ -685,7 +611,7 @@
},
{
"cell_type": "code",
- "execution_count": 21,
+ "execution_count": null,
"id": "4251f544",
"metadata": {},
"outputs": [],
@@ -726,26 +652,10 @@
},
{
"cell_type": "code",
- "execution_count": 22,
+ "execution_count": null,
"id": "736897de",
"metadata": {},
- "outputs": [
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "[[1.9312563653434478, -0.37381092906341173, 0.4178279694822413, 0.0, 0.0, 0.0], [19.65881626964345, -0.28050992995496926, 0.061664536002121184, 0.0, 0.0, 0.0]]\n",
- "assert hit for drone1: \"Unsafe Region\" @ {'ego': State(x=40.10175821673612, y=-0.039154812580084464, z=-7.942172404694119, vx=0.9842831331582693, vy=-0.19685662663165338, vz=0.39999999999998614, craft_mode='Normal', track_mode='T2'), 'others': [State(x=40.30907452806768, y=-0.024596203912877312, z=0.10166446286779385, vx=0.5905698798949595, vy=-0.19685662663165493, vz=-0.40000000000001545, craft_mode='Normal', track_mode='T1')], 'track_map': <tutorial_map.M4 object at 0x000001B2D16192E0>}\n",
- "{'drone1': ['Normal', 'T1'], 'drone2': ['Normal', 'T1']}\n",
- "{'drone1': ('MoveDown', 'M12'), 'drone2': ['Normal', 'T1']}\n",
- "{'drone1': ('MoveUp', 'M10'), 'drone2': ['Normal', 'T1']}\n",
- "{'drone1': ('Normal', 'T2'), 'drone2': ['Normal', 'T1']}\n",
- "assert hit for drone1: \"Unsafe Region\"\n",
- "69\n",
- "{'drone1': ('Normal', 'T0'), 'drone2': ['Normal', 'T1']}\n"
- ]
- }
- ],
+ "outputs": [],
"source": [
"traces_simu = scenario.simulate(60, 0.2)\n",
"traces_veri = scenario.verify(60, 0.2)"
@@ -753,33 +663,10 @@
},
{
"cell_type": "code",
- "execution_count": 23,
+ "execution_count": null,
"id": "e31dc9ff",
"metadata": {},
- "outputs": [
- {
- "name": "stderr",
- "output_type": "stream",
- "text": [
- "ERROR:root:<<Cannot triangulate; no input points\n",
- "ERROR:root:<<Cannot triangulate; no input points\n",
- "ERROR:root:<<Cannot triangulate; no input points\n",
- "ERROR:root:<<Cannot triangulate; no input points\n",
- "ERROR:root:<<Cannot triangulate; no input points\n",
- "ERROR:root:<<Cannot triangulate; no input points\n"
- ]
- },
- {
- "data": {
- "image/png": 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",
- "text/plain": [
- "<PIL.Image.Image image mode=RGB size=1024x768>"
- ]
- },
- "metadata": {},
- "output_type": "display_data"
- }
- ],
+ "outputs": [],
"source": [
"from verse.plotter.plotter3D import *\n",
"import pyvista as pv\n",
@@ -829,6 +716,7 @@
"metadata": {},
"outputs": [],
"source": [
+ "import numpy as np\n",
"def car_dynamics(t, state, u):\n",
" x, y, theta, v = state\n",
" delta, a = u \n",
@@ -849,7 +737,7 @@
},
{
"cell_type": "code",
- "execution_count": 3,
+ "execution_count": 2,
"id": "c1dda851",
"metadata": {},
"outputs": [],
@@ -859,7 +747,7 @@
"import numpy as np \n",
"from scipy.integrate import ode\n",
"\n",
- "def TC_simulate(self, mode: List[str], initialCondition, time_bound, time_step, track_map=None)->np.ndarray:\n",
+ "def TC_simulate(mode: List[str], initialCondition, time_bound, time_step, track_map=None)->np.ndarray:\n",
" time_bound = float(time_bound)\n",
" number_points = int(np.ceil(time_bound/time_step))\n",
" t = [round(i*time_step,10) for i in range(0,number_points)]\n",
@@ -872,8 +760,6 @@
" r.set_initial_value(init).set_f_params([steering, a]) \n",
" res:np.ndarray = r.integrate(r.t + time_step)\n",
" init = res.flatten().tolist()\n",
- " if init[3] < 0:\n",
- " init[3] = 0\n",
" trace.append([t[i] + time_step] + init) \n",
"\n",
" return np.array(trace)\n"
@@ -884,41 +770,75799 @@
"id": "3c2e6579",
"metadata": {},
"source": [
- "With the <code>TC_simulate</code> function specified, we can now combine all parts together to construct a new car agent. An agent class for Verse should have three attributes: 1) <code>id</code>: an unique identifier for each agent in a scenario, 2) <code>controller</code>: a <code>ControllerIR</code> object, which is an intermediate representation of decisionLogic in Verse. It can be constructed automatically by passing in the decision logic code strings described in previous sections, and 3) <code>TC_simulate</code> function. In this example we will create the agent class manually. However, the agent class can also be inherited from the <code>BaseAgent</code> class with proper arguments to automatically having all required fields."
+ "With the <code>TC_simulate</code> function specified, we can now combine all parts together to construct a new car agent. An agent class for Verse should have four attributes: 1) <code>id</code>: an unique identifier for each agent in a scenario, 2) <code>controller</code>: a <code>ControllerIR</code> object, which is an intermediate representation of decisionLogic in Verse. It can be constructed automatically by passing in the decision logic code strings described in previous sections, 3) <code>TC_simulate</code> function, and 4) functions for setting initial condition of the agent, which can be inherited from <code>BaseAgent</code> class. "
]
},
{
"cell_type": "code",
- "execution_count": 4,
+ "execution_count": 5,
"id": "86626628",
"metadata": {},
+ "outputs": [],
+ "source": [
+ "from verse.parser.parser import ControllerIR\n",
+ "from verse.agents import BaseAgent\n",
+ "\n",
+ "class CarAgent(BaseAgent):\n",
+ " def __init__(self, id, code = None, file_name = None):\n",
+ " self.id = id \n",
+ " self.decision_logic = ControllerIR.parse(code, file_name)\n",
+ " self.TC_simulate = TC_simulate"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "2a233433",
+ "metadata": {},
+ "source": [
+ "We are going to test this CarAgent in a two car scenario with simple braking decision logic in an one track map. We can setup the scenario in Verse as we discussed in the previous section and simulate/verify the scenario. The results are visualized below. "
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 6,
+ "metadata": {},
"outputs": [
{
- "name": "stderr",
+ "name": "stdout",
"output_type": "stream",
"text": [
- "`polytope` failed to import `cvxopt.glpk`.\n",
- "will use `scipy.optimize.linprog`\n"
+ "[[0.10779628592289714, -0.47633896494783723, 0.0, 2.0], [15.142047995420299, 0.45265980932133176, 0.0, 1.0]]\n",
+ "{'car1': ['Normal', 'T0'], 'car2': ['Normal', 'T0']}\n",
+ "{'car1': ('Brake', 'T0'), 'car2': ['Normal', 'T0']}\n"
]
+ },
+ {
+ "data": {
+ "application/vnd.plotly.v1+json": {
+ "config": {
+ "plotlyServerURL": "https://plot.ly"
+ },
+ "data": [
+ {
+ "legendgroup": "car1",
+ "legendgrouptitle": {
+ "text": "car1"
+ },
+ "line": {
+ "color": "#CC0000",
+ "width": 3
+ },
+ "mode": "lines",
+ "name": "0.0-7.03-1",
+ "showlegend": false,
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"source": [
- "from verse.parser.parser import ControllerIR\n",
+ "from enum import Enum, auto\n",
"\n",
- "class AgentCar:\n",
- " def __init__(self, id, code = None, file_name = None):\n",
- " self.id = id \n",
- " self.controller = ControllerIR.parse(code, file_name)\n",
- " self.TC_simulate = TC_simulate\n"
+ "class AgentMode(Enum):\n",
+ " Normal = auto()\n",
+ " Brake = auto()\n",
+ "\n",
+ "class TrackMode(Enum):\n",
+ " T0 = auto()\n",
+ "\n",
+ "from verse.scenario import Scenario\n",
+ "from tutorial_map import M1\n",
+ "scenario = Scenario()\n",
+ "scenario.set_map(M1())\n",
+ "\n",
+ "\n",
+ "car1 = CarAgent('car1', file_name=\"dl_sec4.py\")\n",
+ "car1.set_initial([[0,-0.5,0,2],[1,0.5,0,2]], (AgentMode.Normal, TrackMode.T0))\n",
+ "car2 = CarAgent('car2', file_name=\"dl_sec4.py\")\n",
+ "car2.set_initial([[15,-0.5,0,1],[16,0.5,0,1]], (AgentMode.Normal, TrackMode.T0))\n",
+ "scenario.add_agent(car1)\n",
+ "scenario.add_agent(car2)\n",
+ "\n",
+ "traces_simu = scenario.simulate(10, 0.01)\n",
+ "traces_veri = scenario.verify(10, 0.01)\n",
+ "\n",
+ "import plotly.graph_objects as go\n",
+ "from verse.plotter.plotter2D import *\n",
+ "\n",
+ "fig = go.Figure()\n",
+ "fig = simulation_tree(traces_simu, None, fig, 0, 1, [0, 1], 'lines', 'trace')\n",
+ "fig.show()\n",
+ "\n",
+ "fig = go.Figure()\n",
+ "fig = reachtube_tree(traces_veri, None, fig, 0, 1, [0, 1], 'lines', 'trace')\n",
+ "fig.show()"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "# 5. Creating map (work in progress)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "In this section, we will look at how to create a map for Verse by extending the map we used in the previous section from 1 lane to 2 lanes. "
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "The map will contain a set of tracks, that the agent's continuous dynamics roughly follows. In addition, we also assume that an agent's decision logic does not depend on exactly which of the infinitely many tracks it is following, but instead, it depends only on which type of track it is following, or the track mode. In this example, as shown in the figure below, each of the lanes will have a track mode <code>T0</code> and <code>T1</code>. In Verse, we can create the two types of tracks <code>T0</code> and <code>T1</code> using the <code>Lane</code> object, with each of them having one straight segment constructed by <code>StraightLane</code> as shown below. \n"
]
},
{
"cell_type": "code",
"execution_count": null,
- "id": "2a233433",
"metadata": {},
"outputs": [],
- "source": []
+ "source": [
+ "from verse.map.lane_segment import StraightLane\n",
+ "from verse.map.lane import Lane\n",
+ "\n",
+ "segment0 = StraightLane('seg0', [0,0], [500,0], 3)\n",
+ "lane0 = Lane('T0')\n",
+ "segment1 = StraightLane('seg1', [0,3], [500,3], 3)\n",
+ "lane1 = Lane('T1')"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "Verse currently implments <code>StraightLane</code> and <code>CircularLane</code>, which corresponding to straight line and circular curves. In addition, we allow each <code>Lane</code> to have multiple concatenated <code>StraightLane</code> or <code>CircularLane</code>."
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "In addition, we also define the transition between tracks. For example, every track for transitioning from point on <code>T0</code> to a corresponding point on <code>T1</code> has track mode <code>M01</code>. Therefore, in this example, our map will have a total of 4 track modes <code>T0</code>, <code>T1</code>, <code>M01</code>, and <code>M10</code>. The map will have a mapping function <code>g</code>, which can provide the track that the agent is following given a track mode and the agent's current position. \n",
+ "\n",
+ "\n"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "Finally, a Verse agent's decision logic can change its interal or tactical mode (E.g. <code>Normal</code> to <code>SwitchLeft</code>). When an agent changes its tactical mode, it may also update the track it's following and this is encoded in another function <code>h</code>, which takes the current track mode, the current and the next tactical mode, and generates the new track mode the agent should follow. In this example, the agents have tacical mode <code>Normal</code>, <code>SwitchLeft</code> and <code>SwitchRight</code>. Therefore, an example <code>h</code> function looks like following. "
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "h_dict = {\n",
+ " ('T0','Normal','SwitchLeft'): 'M01',\n",
+ " ('T1','Normal','SwitchRight'): 'M10',\n",
+ " ('M01','SwitchLeft','Normal'): 'T1',\n",
+ " ('M10','SwitchRight','Normal'): 'T0',\n",
+ "}\n",
+ "\n",
+ "def h(lane_idx, agent_mode_src, agent_mode_dest):\n",
+ " return self.h_dict[(lane_idx, agent_mode_src, agent_mode_dest)]\n",
+ "\n",
+ "def h_exist(lane_idx, agent_mode_src, agent_mode_dest):\n",
+ " return (lane_idx, agent_mode_src, agent_mode_dest) in h_dict"
+ ]
}
],
"metadata": {
diff --git a/tutorial/dl_sec4.py b/tutorial/dl_sec4.py
new file mode 100644
index 00000000..72803069
--- /dev/null
+++ b/tutorial/dl_sec4.py
@@ -0,0 +1,28 @@
+from enum import Enum, auto
+
+class AgentMode(Enum):
+ Normal = auto()
+ Brake = auto()
+
+class TrackMode(Enum):
+ T0 = auto()
+
+class State:
+ x:float
+ y:float
+ theta:float
+ v:float
+ agent_mode:AgentMode
+ track_mode:TrackMode
+
+ def __init__(self, x, y, theta, v, agent_mode: AgentMode, track_mode: TrackMode):
+ pass
+
+from typing import List
+import copy
+def decisionLogic(ego:State, others: List[State], track_map):
+ output = copy.deepcopy(ego)
+ if ego.agent_mode == AgentMode.Normal:
+ if any( other.x-ego.x< 8 and other.x-ego.x>0 for other in others):
+ output.agent_mode = AgentMode.Brake
+ return output
\ No newline at end of file
diff --git a/tutorial/dl_sec5.py b/tutorial/dl_sec5.py
new file mode 100644
index 00000000..31742f3c
--- /dev/null
+++ b/tutorial/dl_sec5.py
@@ -0,0 +1,61 @@
+from enum import Enum, auto
+
+class AgentMode(Enum):
+ Normal = auto()
+ SwitchLeft = auto()
+ SwitchRight = auto()
+
+class TrackMode(Enum):
+ T0 = auto()
+ T1 = auto()
+ M01 = auto()
+ M10 = auto()
+
+class State:
+ x:float
+ y:float
+ theta:float
+ v:float
+ agent_mode:AgentMode
+ track_mode:TrackMode
+
+ def __init__(self, x, y, theta, v, agent_mode: AgentMode, track_mode: TrackMode):
+ pass
+
+def vehicle_front(ego, others, track_map):
+ res = any((track_map.get_longitudinal_position(other.track_mode, [other.x,other.y]) - track_map.get_longitudinal_position(ego.track_mode, [ego.x,ego.y]) > 3 \
+ and track_map.get_longitudinal_position(other.track_mode, [other.x,other.y]) - track_map.get_longitudinal_position(ego.track_mode, [ego.x,ego.y]) < 5 \
+ and ego.track_mode == other.track_mode) for other in others)
+ return res
+
+def vehicle_close(ego, others):
+ res = any(ego.x-other.x<1.0 and ego.x-other.x>-1.0 and ego.y-other.y<1.0 and ego.y-other.y>-1.0 for other in others)
+ return res
+
+from typing import List
+import copy
+
+def decisionLogic(ego:State, others:List[State], track_map):
+ output = copy.deepcopy(ego)
+ if ego.agent_mode == AgentMode.Normal:
+ if vehicle_front(ego, others, track_map):
+ if track_map.h_exist(ego.track_mode, ego.agent_mode, AgentMode.SwitchLeft):
+ output.agent_mode = AgentMode.SwitchLeft
+ output.track_mode = track_map.h(ego.track_mode, ego.agent_mode, AgentMode.SwitchLeft)
+ if vehicle_front(ego, others, track_map):
+ if track_map.h_exist(ego.track_mode, ego.agent_mode, AgentMode.SwitchRight):
+ output.agent_mode = AgentMode.SwitchRight
+ output.track_mode = track_map.h(ego.track_mode, ego.agent_mode, AgentMode.SwitchRight)
+ lat_dist = track_map.get_lateral_distance(ego.track_mode, [ego.x, ego.y])
+ if ego.agent_mode == AgentMode.SwitchLeft:
+ if lat_dist >= 2.5:
+ output.agent_mode = AgentMode.Normal
+ output.track_mode = track_map.h(ego.track_mode, ego.agent_mode, AgentMode.Normal)
+ if ego.agent_mode == AgentMode.SwitchRight:
+ if lat_dist <= -2.5:
+ output.agent_mode = AgentMode.Normal
+ output.track_mode = track_map.h(ego.track_mode, ego.agent_mode, AgentMode.Normal)
+
+ assert not vehicle_close(ego, others)
+ return output
+
diff --git a/tutorial/map_2lanes_lab.png b/tutorial/map_2lanes_lab.png
new file mode 100644
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diff --git a/tutorial/test.py b/tutorial/test.py
index ad7d435b..e4d30c7a 100644
--- a/tutorial/test.py
+++ b/tutorial/test.py
@@ -1,71 +1,65 @@
-from verse.scenario import Scenario
-from tutorial_map import M4
-scenario = Scenario()
-scenario.set_map(M4())
+import numpy as np
+def car_dynamics(t, state, u):
+ x, y, theta, v = state
+ delta, a = u
+ x_dot = v*np.cos(theta+delta)
+ y_dot = v*np.sin(theta+delta)
+ theta_dot = v/1.75*np.tan(delta)
+ v_dot = a
+ return [x_dot, y_dot, theta_dot, v_dot]
-from enum import Enum, auto
+from tutorial_utils import car_action_handler
+from typing import List
+import numpy as np
+from scipy.integrate import ode
-class CraftMode(Enum):
- Normal = auto()
- MoveUp = auto()
- MoveDown = auto()
+def TC_simulate(mode: List[str], initialCondition, time_bound, time_step, track_map=None)->np.ndarray:
+ time_bound = float(time_bound)
+ number_points = int(np.ceil(time_bound/time_step))
+ t = [round(i*time_step,10) for i in range(0,number_points)]
-class TrackMode(Enum):
- T0 = auto()
- T1 = auto()
- T2 = auto()
- M01 = auto()
- M10 = auto()
- M12 = auto()
- M21 = auto()
+ init = initialCondition
+ trace = [[0]+init]
+ for i in range(len(t)):
+ steering, a = car_action_handler(mode, init, track_map)
+ r = ode(car_dynamics)
+ r.set_initial_value(init).set_f_params([steering, a])
+ res:np.ndarray = r.integrate(r.t + time_step)
+ init = res.flatten().tolist()
+ trace.append([t[i] + time_step] + init)
-class State:
- x: float
- y: float
- z: float
- vx: float
- vy: float
- vz: float
- craft_mode: CraftMode
- track_mode: TrackMode
+ return np.array(trace)
- def __init__(self, x, y, z, vx, vy, vz, craft_mode, track_mode):
- pass
+from verse.parser.parser import ControllerIR
+from verse.agents import BaseAgent
-from tutorial_agent import DroneAgent
-drone1 = DroneAgent(
- 'drone1', file_name="dl_sec2.py", t_v_pair=(1, 1), box_side=[0.4]*3)
-drone1.set_initial(
- [[1.5, -0.5, -0.5, 0, 0, 0], [2.5, 0.5, 0.5, 0, 0, 0]],
- (CraftMode.Normal, TrackMode.T1)
-)
-scenario.add_agent(drone1)
+class CarAgent(BaseAgent):
+ def __init__(self, id, code = None, file_name = None):
+ self.id = id
+ self.decision_logic = ControllerIR.parse(code, file_name)
+ self.TC_simulate = TC_simulate
-drone2 = DroneAgent(
- 'drone2', file_name="dl_sec2.py", t_v_pair=(1, 0.5), box_side=[0.4]*3)
-drone2.set_initial(
- [[19.5, -0.5, -0.5, 0, 0, 0], [20.5, 0.5, 0.5, 0, 0, 0]],
- (CraftMode.Normal, TrackMode.T1)
-)
-scenario.add_agent(drone2)
+from enum import Enum, auto
-scenario.add_agent(drone1)
-scenario.add_agent(drone2)
+class AgentMode(Enum):
+ Normal = auto()
+ Brake = auto()
+
+class TrackMode(Enum):
+ T0 = auto()
-from tutorial_sensor import DefaultSensor
-scenario.set_sensor(DefaultSensor())
+from verse.scenario import Scenario
+from tutorial_map import M1
+scenario = Scenario()
+scenario.set_map(M1())
-traces_simu = scenario.simulate(60, 0.2)
-traces_veri = scenario.verify(60, 0.2)
-from verse.plotter.plotter3D import *
-import pyvista as pv
-import warnings
-warnings.filterwarnings("ignore")
+car1 = CarAgent('car1', file_name="./tutorial/dl_sec4.py")
+car1.set_initial([[0,-0.5,0,2],[1,0.5,0,2]], (AgentMode.Normal, TrackMode.T0))
+car2 = CarAgent('car2', file_name="./tutorial/dl_sec4.py")
+car2.set_initial([[15,-0.5,0,1],[16,0.5,0,1]], (AgentMode.Normal, TrackMode.T0))
+scenario.add_agent(car1)
+scenario.add_agent(car2)
-fig = pv.Plotter()
-fig = plot3dMap(M4(), ax=fig)
-fig = plot3dReachtube(traces_veri, 'drone1', 1, 2, 3, color = 'r', ax=fig)
-fig = plot3dReachtube(traces_veri, 'drone2', 1, 2, 3, color = 'b', ax=fig)
-fig.set_background('#e0e0e0')
-fig.show()
\ No newline at end of file
+traces_simu = scenario.simulate(10, 0.01)
+traces_veri = scenario.verify(10, 0.01)
\ No newline at end of file
diff --git a/tutorial/tutorial_agent.py b/tutorial/tutorial_agent.py
index 335722bb..aaa8c66a 100644
--- a/tutorial/tutorial_agent.py
+++ b/tutorial/tutorial_agent.py
@@ -10,7 +10,7 @@ from verse import BaseAgent
from verse import LaneMap
from verse.map.lane_map_3d import LaneMap_3d
-class AgentCar(BaseAgent):
+class CarAgent(BaseAgent):
def __init__(self, id, code = None, file_name = None, initial_state = None, initial_mode = None):
super().__init__(id, code, file_name, initial_state=initial_state, initial_mode=initial_mode)
self.switch_duration = 0
@@ -25,33 +25,34 @@ class AgentCar(BaseAgent):
v_dot = a
return [x_dot, y_dot, theta_dot, v_dot]
- def action_handler(self, mode: List[str], state, track_map:LaneMap)->Tuple[float, float]:
+ def action_handler(self, mode: List[str], state, lane_map)->Tuple[float, float]:
x,y,theta,v = state
vehicle_mode = mode[0]
vehicle_lane = mode[1]
vehicle_pos = np.array([x,y])
a = 0
if vehicle_mode == "Normal":
- d = -track_map.get_lateral_distance(vehicle_lane, vehicle_pos)
- self.switch_duration = 0
+ d = -lane_map.get_lateral_distance(vehicle_lane, vehicle_pos)
+ elif vehicle_mode == "SwitchLeft":
+ d = -lane_map.get_lateral_distance(vehicle_lane, vehicle_pos) + lane_map.get_lane_width(vehicle_lane)
+ elif vehicle_mode == "SwitchRight":
+ d = -lane_map.get_lateral_distance(vehicle_lane, vehicle_pos) - lane_map.get_lane_width(vehicle_lane)
elif vehicle_mode == "Brake":
- d = -track_map.get_lateral_distance(vehicle_lane, vehicle_pos)
- if v>0:
- a = -1
- if v<0.01:
- a=0
- else:
- a = 1
- if v>-0.01:
- a=0
- self.switch_duration = 0
+ d = -lane_map.get_lateral_distance(vehicle_lane, vehicle_pos)
+ a = -1
+ elif vehicle_mode == "Accel":
+ d = -lane_map.get_lateral_distance(vehicle_lane, vehicle_pos)
+ a = 1
+ elif vehicle_mode == 'Stop':
+ d = -lane_map.get_lateral_distance(vehicle_lane, vehicle_pos)
+ a = 0
else:
raise ValueError(f'Invalid mode: {vehicle_mode}')
- psi = track_map.get_lane_heading(vehicle_lane, vehicle_pos)-theta
+ psi = lane_map.get_lane_heading(vehicle_lane, vehicle_pos)-theta
steering = psi + np.arctan2(0.45*d, v)
steering = np.clip(steering, -0.61, 0.61)
- return steering, a
+ return steering, a
def TC_simulate(self, mode: List[str], initialCondition, time_bound, time_step, track_map:LaneMap=None)->np.ndarray:
time_bound = float(time_bound)
diff --git a/tutorial/tutorial_map.py b/tutorial/tutorial_map.py
index c098eef6..cc1408e2 100644
--- a/tutorial/tutorial_map.py
+++ b/tutorial/tutorial_map.py
@@ -142,4 +142,31 @@ class M4(LaneMap_3d):
lane = 'T1'
elif lane_idx[-1] == '2':
lane = 'T2'
- return lane
\ No newline at end of file
+ return lane
+
+class M5(LaneMap):
+ def __init__(self):
+ super().__init__()
+ segment0 = StraightLane(
+ 'Seg0',
+ [0,0],
+ [500,0],
+ 3
+ )
+ lane0 = Lane('T0', [segment0])
+ segment0 = StraightLane(
+ 'Seg0',
+ [0,3],
+ [500,3],
+ 3
+ )
+ lane1 = Lane('T1', [segment0])
+ # segment2 = LaneSegment('Lane1', 3)
+ # self.add_lanes([segment1,segment2])
+ self.add_lanes([lane0, lane1])
+ self.h_dict = {
+ ("T0","Normal","SwitchLeft"):"M01",
+ ("T1","Normal","SwitchRight"):"M10",
+ ("M01","SwitchLeft","Normal"):"T1",
+ ("M10","SwitchRight","Normal"):"T0",
+ }
\ No newline at end of file
diff --git a/tutorial/tutorial_sensor.py b/tutorial/tutorial_sensor.py
index 426326ec..733e5a64 100644
--- a/tutorial/tutorial_sensor.py
+++ b/tutorial/tutorial_sensor.py
@@ -59,7 +59,7 @@ class DefaultSensor():
for agent_id in state_dict:
if agent_id == agent.id:
# Get type of ego
- controller_args = agent.controller.args
+ controller_args = agent.decision_logic.args
arg_type = None
for arg in controller_args:
if arg.name == 'ego':
@@ -68,13 +68,13 @@ class DefaultSensor():
if arg_type is None:
continue
raise ValueError(f"Invalid arg for ego")
- cont_var = agent.controller.state_defs[arg_type].cont
- disc_var = agent.controller.state_defs[arg_type].disc
- stat_var = agent.controller.state_defs[arg_type].static
+ cont_var = agent.decision_logic.state_defs[arg_type].cont
+ disc_var = agent.decision_logic.state_defs[arg_type].disc
+ stat_var = agent.decision_logic.state_defs[arg_type].static
set_states_2d(
cont, disc, 'ego', state_dict[agent_id], cont_var, disc_var, stat_var)
else:
- controller_args = agent.controller.args
+ controller_args = agent.decision_logic.args
arg_type = None
arg_name = None
for arg in controller_args:
@@ -85,9 +85,9 @@ class DefaultSensor():
if arg_type is None:
continue
raise ValueError(f"Invalid arg for others")
- cont_var = agent.controller.state_defs[arg_type].cont
- disc_var = agent.controller.state_defs[arg_type].disc
- stat_var = agent.controller.state_defs[arg_type].static
+ cont_var = agent.decision_logic.state_defs[arg_type].cont
+ disc_var = agent.decision_logic.state_defs[arg_type].disc
+ stat_var = agent.decision_logic.state_defs[arg_type].static
add_states_2d(
cont, disc, arg_name, state_dict[agent_id], cont_var, disc_var, stat_var)
@@ -95,7 +95,7 @@ class DefaultSensor():
for agent_id in state_dict:
if agent_id == agent.id:
# Get type of ego
- controller_args = agent.controller.args
+ controller_args = agent.decision_logic.args
arg_type = None
for arg in controller_args:
if arg.name == 'ego':
@@ -103,13 +103,13 @@ class DefaultSensor():
break
if arg_type is None:
raise ValueError(f"Invalid arg for ego")
- cont_var = agent.controller.state_defs[arg_type].cont
- disc_var = agent.controller.state_defs[arg_type].disc
- stat_var = agent.controller.state_defs[arg_type].static
+ cont_var = agent.decision_logic.state_defs[arg_type].cont
+ disc_var = agent.decision_logic.state_defs[arg_type].disc
+ stat_var = agent.decision_logic.state_defs[arg_type].static
set_states_3d(
cont, disc, 'ego', state_dict[agent_id], cont_var, disc_var, stat_var)
else:
- controller_args = agent.controller.args
+ controller_args = agent.decision_logic.args
arg_type = None
arg_name = None
for arg in controller_args:
@@ -119,9 +119,9 @@ class DefaultSensor():
break
if arg_type is None:
raise ValueError(f"Invalid arg for others")
- cont_var = agent.controller.state_defs[arg_type].cont
- disc_var = agent.controller.state_defs[arg_type].disc
- stat_var = agent.controller.state_defs[arg_type].static
+ cont_var = agent.decision_logic.state_defs[arg_type].cont
+ disc_var = agent.decision_logic.state_defs[arg_type].disc
+ stat_var = agent.decision_logic.state_defs[arg_type].static
add_states_3d(cont, disc, arg_name, state_dict[agent_id], cont_var, disc_var, stat_var)
return cont, disc, len_dict
diff --git a/tutorial/tutorial_utils.py b/tutorial/tutorial_utils.py
index 78d419aa..a2a8421a 100644
--- a/tutorial/tutorial_utils.py
+++ b/tutorial/tutorial_utils.py
@@ -10,7 +10,7 @@ def car_dynamics(t, state, u):
v_dot = a
return [x_dot, y_dot, theta_dot, v_dot]
-def car_action_handler(self, mode: List[str], state, lane_map)->Tuple[float, float]:
+def car_action_handler(mode: List[str], state, lane_map)->Tuple[float, float]:
x,y,theta,v = state
vehicle_mode = mode[0]
vehicle_lane = mode[1]
@@ -18,25 +18,19 @@ def car_action_handler(self, mode: List[str], state, lane_map)->Tuple[float, flo
a = 0
if vehicle_mode == "Normal":
d = -lane_map.get_lateral_distance(vehicle_lane, vehicle_pos)
- self.switch_duration = 0
elif vehicle_mode == "SwitchLeft":
d = -lane_map.get_lateral_distance(vehicle_lane, vehicle_pos) + lane_map.get_lane_width(vehicle_lane)
- self.switch_duration += 0.1
elif vehicle_mode == "SwitchRight":
d = -lane_map.get_lateral_distance(vehicle_lane, vehicle_pos) - lane_map.get_lane_width(vehicle_lane)
- self.switch_duration += 0.1
elif vehicle_mode == "Brake":
d = -lane_map.get_lateral_distance(vehicle_lane, vehicle_pos)
a = -1
- self.switch_duration = 0
elif vehicle_mode == "Accel":
d = -lane_map.get_lateral_distance(vehicle_lane, vehicle_pos)
a = 1
- self.switch_duration = 0
elif vehicle_mode == 'Stop':
d = -lane_map.get_lateral_distance(vehicle_lane, vehicle_pos)
a = 0
- self.switch_duration = 0
else:
raise ValueError(f'Invalid mode: {vehicle_mode}')
diff --git a/verse/agents/base_agent.py b/verse/agents/base_agent.py
index 097dd9cc..e073abc4 100644
--- a/verse/agents/base_agent.py
+++ b/verse/agents/base_agent.py
@@ -24,7 +24,7 @@ class BaseAgent:
file_name: str
file name to the python controller
"""
- self.controller: ControllerIR = ControllerIR.parse(code, file_name)
+ self.decision_logic: ControllerIR = ControllerIR.parse(code, file_name)
self.id = id
self.init_cont = copy.deepcopy(initial_state)
self.init_disc = copy.deepcopy(initial_mode)
diff --git a/verse/agents/example_agent/car_agent.py b/verse/agents/example_agent/car_agent.py
index 3aff631f..cf587e6e 100644
--- a/verse/agents/example_agent/car_agent.py
+++ b/verse/agents/example_agent/car_agent.py
@@ -11,7 +11,7 @@ from verse.parser import ControllerIR
class NPCAgent(BaseAgent):
def __init__(self, id, initial_state=None, initial_mode=None):
self.id = id
- self.controller = ControllerIR.empty()
+ self.decision_logic = ControllerIR.empty()
self.set_initial_state(initial_state)
self.set_initial_mode(initial_mode)
self.set_static_parameter(None)
diff --git a/verse/agents/example_agent/sign_agent.py b/verse/agents/example_agent/sign_agent.py
index 132e3a91..089609c6 100644
--- a/verse/agents/example_agent/sign_agent.py
+++ b/verse/agents/example_agent/sign_agent.py
@@ -6,7 +6,7 @@ from verse.parser import ControllerIR
class SignAgent(BaseAgent):
def __init__(self, id):
self.id = id
- self.controller = ControllerIR.empty()
+ self.decision_logic = ControllerIR.empty()
def TC_simulate(self, mode, init, time_horizon, time_step, map=None):
number_points = int(np.ceil(float(time_horizon)/time_step))
diff --git a/verse/analysis/analysis_tree.py b/verse/analysis/analysis_tree.py
index a541de59..dd04efb9 100644
--- a/verse/analysis/analysis_tree.py
+++ b/verse/analysis/analysis_tree.py
@@ -59,21 +59,21 @@ class AnalysisTreeNode:
return rst_dict
def get_track(self, agent_id, D):
- if 'TrackMode' not in self.agent[agent_id].controller.mode_defs:
+ if 'TrackMode' not in self.agent[agent_id].decision_logic.mode_defs:
return ""
for d in D:
- if d in self.agent[agent_id].controller.mode_defs['TrackMode'].modes:
+ if d in self.agent[agent_id].decision_logic.mode_defs['TrackMode'].modes:
return d
return ""
def get_mode(self, agent_id, D):
res = []
- if 'TrackMode' not in self.agent[agent_id].controller.mode_defs:
+ if 'TrackMode' not in self.agent[agent_id].decision_logic.mode_defs:
if len(D)==1:
return D[0]
return D
for d in D:
- if d not in self.agent[agent_id].controller.mode_defs['TrackMode'].modes:
+ if d not in self.agent[agent_id].decision_logic.mode_defs['TrackMode'].modes:
res.append(d)
if len(res) == 1:
return res[0]
diff --git a/verse/analysis/incremental.py b/verse/analysis/incremental.py
index 52d9dd05..505ae7d9 100644
--- a/verse/analysis/incremental.py
+++ b/verse/analysis/incremental.py
@@ -51,7 +51,7 @@ def to_simulate(old_agents: Dict[str, BaseAgent], new_agents: Dict[str, BaseAgen
removed_paths, added_paths, reset_changed_paths = [], [], []
for agent_id, old_agent in old_agents.items():
new_agent = new_agents[agent_id]
- old_ctlr, new_ctlr = old_agent.controller, new_agent.controller
+ old_ctlr, new_ctlr = old_agent.decision_logic, new_agent.decision_logic
assert old_ctlr.args == new_ctlr.args
def group_by_var(ctlr: ControllerIR) -> Dict[str, List[ModePath]]:
grouped = defaultdict(list)
@@ -149,7 +149,7 @@ class SimTraceCache:
for i, val in enumerate(init):
if i == len(init) - 1:
transitions = convert_sim_trans(agent_id, transit_agents, node.init, transition, trans_ind)
- entry = CachedSegment(trace, assert_hits.get(agent_id), transitions, node.agent[agent_id].controller, run_num, node.id)
+ entry = CachedSegment(trace, assert_hits.get(agent_id), transitions, node.agent[agent_id].decision_logic, run_num, node.id)
tree[val - _EPSILON:val + _EPSILON] = entry
return entry
else:
@@ -247,7 +247,7 @@ class ReachTubeCache:
for i, (low, high) in enumerate(init):
if i == len(init) - 1:
transitions = convert_reach_trans(agent_id, transit_agents, node.init, transition, trans_ind)
- entry = CachedRTTrans(assert_hits.get(agent_id), transitions, node.agent[agent_id].controller, run_num, node.id)
+ entry = CachedRTTrans(assert_hits.get(agent_id), transitions, node.agent[agent_id].decision_logic, run_num, node.id)
tree[low:high + _EPSILON] = entry
return entry
else:
diff --git a/verse/automaton/guard.py b/verse/automaton/guard.py
index d51fa68b..7c1d15c4 100644
--- a/verse/automaton/guard.py
+++ b/verse/automaton/guard.py
@@ -577,9 +577,9 @@ class GuardExpressionAst:
root = ast.parse('False').body[0].value
else:
# # TODO-PARSER: Handle This
- # for mode_name in agent.controller.mode_defs:
+ # for mode_name in agent.decision_logic.mode_defs:
# # TODO-PARSER: Handle This
- # if res in agent.controller.mode_defs[mode_name].modes:
+ # if res in agent.decision_logic.mode_defs[mode_name].modes:
# res = mode_name+'.'+res
# break
root = ast.parse(str(res)).body[0].value
@@ -592,14 +592,14 @@ class GuardExpressionAst:
if expr in disc_var_dict:
val = disc_var_dict[expr]
# TODO-PARSER: Handle This
- # for mode_name in agent.controller.mode_defs:
+ # for mode_name in agent.decision_logic.mode_defs:
# # TODO-PARSER: Handle This
- # if val in agent.controller.mode_defs[mode_name].modes:
+ # if val in agent.decision_logic.mode_defs[mode_name].modes:
# val = mode_name+'.'+val
# break
return val, root
# TODO-PARSER: Handle This
- elif root.value.id in agent.controller.mode_defs:
+ elif root.value.id in agent.decision_logic.mode_defs:
return expr, root
else:
return True, root
@@ -621,9 +621,9 @@ class GuardExpressionAst:
if expr in disc_var_dict:
val = disc_var_dict[expr]
# # TODO-PARSER: Handle This
- # for mode_name in agent.controller.mode_defs:
+ # for mode_name in agent.decision_logic.mode_defs:
# # TODO-PARSER: Handle This
- # if val in agent.controller.mode_defs[mode_name].modes:
+ # if val in agent.decision_logic.mode_defs[mode_name].modes:
# val = mode_name + '.' + val
# break
return val, root
@@ -704,9 +704,9 @@ class GuardExpressionAst:
expr = expr.replace(arg, str(cnts_var_dict[arg]))
res = eval(expr)
# TODO-PARSER: Handle This
- for mode_name in agent.controller.mode_defs:
+ for mode_name in agent.decision_logic.mode_defs:
# TODO-PARSER: Handle This
- if res in agent.controller.mode_defs[mode_name].modes:
+ if res in agent.decision_logic.mode_defs[mode_name].modes:
res = mode_name+'.'+res
break
return res
@@ -716,9 +716,9 @@ class GuardExpressionAst:
if expr in disc_var_dict:
val = disc_var_dict[expr]
# TODO-PARSER: Handle This
- for mode_name in agent.controller.mode_defs:
+ for mode_name in agent.decision_logic.mode_defs:
# TODO-PARSER: Handle This
- if val in agent.controller.mode_defs[mode_name].modes:
+ if val in agent.decision_logic.mode_defs[mode_name].modes:
val = mode_name+'.'+val
break
return val
@@ -727,7 +727,7 @@ class GuardExpressionAst:
return val
# TODO-PARSER: Handle This
- elif root.value.id in agent.controller.mode_defs:
+ elif root.value.id in agent.decision_logic.mode_defs:
return expr
elif isinstance(root, ast.Constant):
return root.value
@@ -747,9 +747,9 @@ class GuardExpressionAst:
elif variable in disc_var_dict:
val = disc_var_dict[variable]
# TODO-PARSER: Handle This
- for mode_name in agent.controller.mode_defs:
+ for mode_name in agent.decision_logic.mode_defs:
# TODO-PARSER: Handle This
- if val in agent.controller.mode_defs[mode_name].modes:
+ if val in agent.decision_logic.mode_defs[mode_name].modes:
val = mode_name+'.'+val
break
return val
diff --git a/verse/automaton/reset.py b/verse/automaton/reset.py
index efc5541b..5b9e6fde 100644
--- a/verse/automaton/reset.py
+++ b/verse/automaton/reset.py
@@ -31,18 +31,18 @@ class ResetExpression():
# agent_state_upper = []
# # TODO-PARSER: Handle This
-# for var in agent.controller.vars_dict['ego'].cont:
+# for var in agent.decision_logic.vars_dict['ego'].cont:
# agent_state_lower.append(continuous_variable_dict['ego.'+var][0])
# agent_state_upper.append(continuous_variable_dict['ego.'+var][1])
# # TODO-PARSER: Handle This
-# assert len(agent_state_lower) == len(agent_state_upper) == len(agent.controller.vars_dict['ego'].cont)
+# assert len(agent_state_lower) == len(agent_state_upper) == len(agent.decision_logic.vars_dict['ego'].cont)
# for expr in self.expr_list:
# if 'mode' not in expr:
# tmp = expr.split('=')
# lhs, rhs = tmp[0], tmp[1]
# # TODO-PARSER: Handle This
-# for lhs_idx, cts_variable in enumerate(agent.controller.vars_dict['ego'].cont):
+# for lhs_idx, cts_variable in enumerate(agent.decision_logic.vars_dict['ego'].cont):
# if "output."+cts_variable == lhs:
# break
@@ -84,7 +84,7 @@ class ResetExpression():
# for reset in self.expr_list:
# if "mode" in reset:
# # TODO-PARSER: Handle This
-# for i, discrete_variable_ego in enumerate(agent.controller.vars_dict['ego'].disc):
+# for i, discrete_variable_ego in enumerate(agent.decision_logic.vars_dict['ego'].disc):
# if discrete_variable_ego in reset:
# break
# tmp = reset.split('=')
@@ -99,7 +99,7 @@ class ResetExpression():
# else:
# tmp = tmp[1].split('.')
# # TODO-PARSER: Handle This
-# if tmp[0].strip(' ') in agent.controller.modes:
+# if tmp[0].strip(' ') in agent.decision_logic.modes:
# possible_dest[i] = [tmp[1]]
# all_dest = itertools.product(*possible_dest)
# res = []
diff --git a/verse/parser/parser.py b/verse/parser/parser.py
index 9a6b8aba..7373e5da 100644
--- a/verse/parser/parser.py
+++ b/verse/parser/parser.py
@@ -788,6 +788,6 @@ if __name__ == "__main__":
e = Env.parse(fn=fn)
e.dump()
ir = e.to_ir()
- print(ControllerIR.dump(ir.controller.body, False))
+ print(ControllerIR.dump(ir.decision_logic.body, False))
for a in ir.asserts:
print(f"assert {ControllerIR.dump(a.cond, False)}, '{a.label}'")
diff --git a/verse/scenario/scenario.py b/verse/scenario/scenario.py
index 252e061b..21928f9c 100644
--- a/verse/scenario/scenario.py
+++ b/verse/scenario/scenario.py
@@ -27,7 +27,7 @@ def red(s):
return "\x1b[31m" + s + "\x1b[0m"
def pack_env(agent: BaseAgent, ego_ty_name: str, cont: Dict[str, float], disc: Dict[str, str], track_map):
- state_ty = None #namedtuple(ego_ty_name, agent.controller.state_defs[ego_ty_name].all_vars())
+ state_ty = None #namedtuple(ego_ty_name, agent.decision_logic.state_defs[ego_ty_name].all_vars())
packed: DefaultDict[str, Any] = defaultdict(dict)
# packed = {}
for e in [cont, disc]:
@@ -36,7 +36,7 @@ def pack_env(agent: BaseAgent, ego_ty_name: str, cont: Dict[str, float], disc: D
packed[k1][k2] = v
ego_keys, ego_vals = tuple(map(list, zip(*packed[EGO].items())))
state_ty = namedtuple(ego_ty_name, ego_keys)
- for arg in agent.controller.args:
+ for arg in agent.decision_logic.args:
if "map" in arg.name:
packed[arg.name] = track_map
elif arg.name != EGO:
@@ -61,11 +61,11 @@ def check_sim_transitions(agent: BaseAgent, guards: List[Tuple], cont, disc, map
satisfied_guard = []
agent_id = agent.id
# Unsafety checking
- ego_ty_name = find(agent.controller.args, lambda a: a.name == EGO).typ
+ ego_ty_name = find(agent.decision_logic.args, lambda a: a.name == EGO).typ
packed_env = pack_env(agent, ego_ty_name, cont, disc, map)
# Check safety conditions
- for assertion in agent.controller.asserts:
+ for assertion in agent.decision_logic.asserts:
if eval(assertion.pre, packed_env):
if not eval(assertion.cond, packed_env):
del packed_env["__builtins__"]
@@ -97,7 +97,7 @@ def check_sim_transitions(agent: BaseAgent, guards: List[Tuple], cont, disc, map
for j, (reset_idx, path) in enumerate(pos):
reset_variable = list(all_resets.keys())[j]
res = eval(all_resets[reset_variable][reset_idx][0], packed_env)
- ego_type = agent.controller.state_defs[ego_ty_name]
+ ego_type = agent.decision_logic.state_defs[ego_ty_name]
if "mode" in reset_variable:
var_loc = ego_type.disc.index(reset_variable)
assert not isinstance(res, list), res
@@ -161,16 +161,16 @@ class Scenario:
# Update the lane mode field in the agent
self.update_agent_lane_mode(agent, self.map)
self.agent_dict[agent.id] = agent
- if agent.init_cont is not None:
+ if hasattr(agent, 'init_cont') and agent.init_cont is not None:
self.init_dict[agent.id] = copy.deepcopy(agent.init_cont)
- if agent.init_disc is not None:
+ if hasattr(agent, 'init_disc') and agent.init_disc is not None:
self.init_mode_dict[agent.id] = copy.deepcopy(agent.init_disc)
- if agent.static_parameters is not None:
+ if hasattr(agent, 'static_parameters') and agent.static_parameters is not None:
self.static_dict[agent.id] = copy.deepcopy(agent.static_parameters)
else:
self.static_dict[agent.id] = []
- if agent.uncertain_parameters is not None:
+ if hasattr(agent, 'uncertain_parameters') and agent.uncertain_parameters is not None:
self.uncertain_param_dict[agent.id] = copy.deepcopy(agent.uncertain_parameters)
else:
self.uncertain_param_dict[agent.id] = []
@@ -179,25 +179,25 @@ class Scenario:
# TODO-PARSER: update this function
def update_agent_lane_mode(self, agent: BaseAgent, track_map: LaneMap):
for lane_id in track_map.lane_dict:
- if 'TrackMode' in agent.controller.mode_defs and lane_id not in agent.controller.mode_defs['TrackMode'].modes:
- agent.controller.mode_defs['TrackMode'].modes.append(lane_id)
- # mode_vals = list(agent.controller.modes.values())
- # agent.controller.vertices = list(itertools.product(*mode_vals))
- # agent.controller.vertexStrings = [','.join(elem) for elem in agent.controller.vertices]
+ if 'TrackMode' in agent.decision_logic.mode_defs and lane_id not in agent.decision_logic.mode_defs['TrackMode'].modes:
+ agent.decision_logic.mode_defs['TrackMode'].modes.append(lane_id)
+ # mode_vals = list(agent.decision_logic.modes.values())
+ # agent.decision_logic.vertices = list(itertools.product(*mode_vals))
+ # agent.decision_logic.vertexStrings = [','.join(elem) for elem in agent.decision_logic.vertices]
def set_init_single(self, agent_id, init: list, init_mode: tuple, static=[], uncertain_param=[]):
assert agent_id in self.agent_dict, 'agent_id not found'
agent = self.agent_dict[agent_id]
assert len(init) == 1 or len(
init) == 2, 'the length of init should be 1 or 2'
- # print(agent.controller.state_defs.values())
- if agent.controller != agent.controller.empty():
+ # print(agent.decision_logic.state_defs.values())
+ if agent.decision_logic != agent.decision_logic.empty():
for i in init:
assert len(i) == len(
- list(agent.controller.state_defs.values())[0].cont), 'the length of element in init not fit the number of continuous variables'
- # print(agent.controller.mode_defs)
+ list(agent.decision_logic.state_defs.values())[0].cont), 'the length of element in init not fit the number of continuous variables'
+ # print(agent.decision_logic.mode_defs)
assert len(init_mode) == len(
- list(agent.controller.state_defs.values())[0].disc), 'the length of element in init_mode not fit the number of discrete variables'
+ list(agent.decision_logic.state_defs.values())[0].disc), 'the length of element in init_mode not fit the number of discrete variables'
if len(init) == 1:
init = init+init
self.init_dict[agent_id] = copy.deepcopy(init)
@@ -305,7 +305,7 @@ class Scenario:
dest = copy.deepcopy(agent_mode)
possible_dest = [[elem] for elem in dest]
- ego_type = find(agent.controller.args, lambda a: a.name == EGO).typ
+ ego_type = find(agent.decision_logic.args, lambda a: a.name == EGO).typ
rect = copy.deepcopy([agent_state[0][1:], agent_state[1][1:]])
# The reset_list here are all the resets for a single transition. Need to evaluate each of them
@@ -317,7 +317,7 @@ class Scenario:
# First get the transition destinations
if "mode" in reset_variable:
found = False
- for var_loc, discrete_variable_ego in enumerate(agent.controller.state_defs[ego_type].disc):
+ for var_loc, discrete_variable_ego in enumerate(agent.decision_logic.state_defs[ego_type].disc):
if discrete_variable_ego == reset_variable:
found = True
break
@@ -338,7 +338,7 @@ class Scenario:
possible_dest[var_loc] = res
else:
expr = tmp
- if expr[0].strip(' ') in agent.controller.mode_defs:
+ if expr[0].strip(' ') in agent.decision_logic.mode_defs:
possible_dest[var_loc] = [expr[1]]
# Assume linear function for continuous variables
@@ -346,7 +346,7 @@ class Scenario:
lhs = reset_variable
rhs = expr
found = False
- for lhs_idx, cts_variable in enumerate(agent.controller.state_defs[ego_type].cont):
+ for lhs_idx, cts_variable in enumerate(agent.decision_logic.state_defs[ego_type].cont):
if cts_variable == lhs:
found = True
break
@@ -416,7 +416,7 @@ class Scenario:
cached_trans = defaultdict(list)
if not cache:
- paths = [(agent, p) for agent in node.agent.values() for p in agent.controller.paths]
+ paths = [(agent, p) for agent in node.agent.values() for p in agent.decision_logic.paths]
else:
_transitions = [(aid, trans) for aid, seg in cache.items() for trans in seg.transitions if sim_trans_suit(trans.inits, node.init)]
# pp(("cached trans", _transitions))
@@ -441,7 +441,7 @@ class Scenario:
path_transitions[p.cond] = max(path_transitions[p.cond], tran.transition)
for agent_id, segment in cache.items():
agent = node.agent[agent_id]
- if len(agent.controller.args) == 0:
+ if len(agent.decision_logic.args) == 0:
continue
state_dict = {aid: (node.trace[aid][0], node.mode[aid], node.static[aid]) for aid in node.agent}
agent_paths = dedup([p for tran in segment.transitions for p in tran.paths], lambda i: (i.var, i.cond, i.val))
@@ -451,7 +451,7 @@ class Scenario:
for agent, path in paths:
# Get guard
- if len(agent.controller.args) == 0:
+ if len(agent.decision_logic.args) == 0:
continue
agent_id = agent.id
agent_mode = node.mode[agent_id]
@@ -508,7 +508,7 @@ class Scenario:
cached_trans = defaultdict(list)
if not cache:
- paths = [(agent, p) for agent in node.agent.values() for p in agent.controller.paths]
+ paths = [(agent, p) for agent in node.agent.values() for p in agent.decision_logic.paths]
else:
# _transitions = [trans.transition for seg in cache.values() for trans in seg.transitions]
@@ -529,7 +529,7 @@ class Scenario:
path_transitions[p.cond] = max(path_transitions[p.cond], tran.transition)
for agent_id, segment in cache.items():
agent = node.agent[agent_id]
- if len(agent.controller.args) == 0:
+ if len(agent.decision_logic.args) == 0:
continue
state_dict = {aid: (node.trace[aid][0], node.mode[aid], node.static[aid]) for aid in node.agent}
@@ -554,7 +554,7 @@ class Scenario:
# if len(trace) < 2:
# pp(("weird state", aid, trace))
for agent, path in paths:
- if len(agent.controller.args) == 0:
+ if len(agent.decision_logic.args) == 0:
continue
agent_id = agent.id
state_dict = {aid: (node.trace[aid][0:2], node.mode[aid], node.static[aid]) for aid in node.agent}
@@ -589,7 +589,7 @@ class Scenario:
asserts = defaultdict(list)
for agent_id in self.agent_dict.keys():
agent: BaseAgent = self.agent_dict[agent_id]
- if len(agent.controller.args) == 0:
+ if len(agent.decision_logic.args) == 0:
continue
agent_state, agent_mode, agent_static = state_dict[agent_id]
# if np.array(agent_state).ndim != 2:
@@ -598,7 +598,7 @@ class Scenario:
cont_vars, disc_vars, len_dict = self.sensor.sense(self, agent, state_dict, self.map)
resets = defaultdict(list)
# Check safety conditions
- for i, a in enumerate(agent.controller.asserts_veri):
+ for i, a in enumerate(agent.decision_logic.asserts_veri):
pre_expr = a.pre
def eval_expr(expr):
diff --git a/verse/sensor/base_sensor.py b/verse/sensor/base_sensor.py
index c7272703..c97942f8 100644
--- a/verse/sensor/base_sensor.py
+++ b/verse/sensor/base_sensor.py
@@ -59,7 +59,7 @@ class BaseSensor():
for agent_id in state_dict:
if agent_id == agent.id:
# Get type of ego
- controller_args = agent.controller.args
+ controller_args = agent.decision_logic.args
arg_type = None
for arg in controller_args:
if arg.name == 'ego':
@@ -68,13 +68,13 @@ class BaseSensor():
if arg_type is None:
continue
raise ValueError(f"Invalid arg for ego")
- cont_var = agent.controller.state_defs[arg_type].cont
- disc_var = agent.controller.state_defs[arg_type].disc
- stat_var = agent.controller.state_defs[arg_type].static
+ cont_var = agent.decision_logic.state_defs[arg_type].cont
+ disc_var = agent.decision_logic.state_defs[arg_type].disc
+ stat_var = agent.decision_logic.state_defs[arg_type].static
set_states_2d(
cont, disc, 'ego', state_dict[agent_id], cont_var, disc_var, stat_var)
else:
- controller_args = agent.controller.args
+ controller_args = agent.decision_logic.args
arg_type = None
arg_name = None
for arg in controller_args:
@@ -85,9 +85,9 @@ class BaseSensor():
if arg_type is None:
continue
raise ValueError(f"Invalid arg for others")
- cont_var = agent.controller.state_defs[arg_type].cont
- disc_var = agent.controller.state_defs[arg_type].disc
- stat_var = agent.controller.state_defs[arg_type].static
+ cont_var = agent.decision_logic.state_defs[arg_type].cont
+ disc_var = agent.decision_logic.state_defs[arg_type].disc
+ stat_var = agent.decision_logic.state_defs[arg_type].static
add_states_2d(
cont, disc, arg_name, state_dict[agent_id], cont_var, disc_var, stat_var)
@@ -95,7 +95,7 @@ class BaseSensor():
for agent_id in state_dict:
if agent_id == agent.id:
# Get type of ego
- controller_args = agent.controller.args
+ controller_args = agent.decision_logic.args
arg_type = None
for arg in controller_args:
if arg.name == 'ego':
@@ -103,13 +103,13 @@ class BaseSensor():
break
if arg_type is None:
raise ValueError(f"Invalid arg for ego")
- cont_var = agent.controller.state_defs[arg_type].cont
- disc_var = agent.controller.state_defs[arg_type].disc
- stat_var = agent.controller.state_defs[arg_type].static
+ cont_var = agent.decision_logic.state_defs[arg_type].cont
+ disc_var = agent.decision_logic.state_defs[arg_type].disc
+ stat_var = agent.decision_logic.state_defs[arg_type].static
set_states_3d(
cont, disc, 'ego', state_dict[agent_id], cont_var, disc_var, stat_var)
else:
- controller_args = agent.controller.args
+ controller_args = agent.decision_logic.args
arg_type = None
arg_name = None
for arg in controller_args:
@@ -119,9 +119,9 @@ class BaseSensor():
break
if arg_type is None:
raise ValueError(f"Invalid arg for others")
- cont_var = agent.controller.state_defs[arg_type].cont
- disc_var = agent.controller.state_defs[arg_type].disc
- stat_var = agent.controller.state_defs[arg_type].static
+ cont_var = agent.decision_logic.state_defs[arg_type].cont
+ disc_var = agent.decision_logic.state_defs[arg_type].disc
+ stat_var = agent.decision_logic.state_defs[arg_type].static
add_states_3d(cont, disc, arg_name, state_dict[agent_id], cont_var, disc_var, stat_var)
return cont, disc, len_dict
--
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