Readme

This repository constructs a rule-based framework for autonomous driving, including:

• path planning based on spline and Bezier curve
• velocity profile based on quintic polynomial optimization
• linear MPC and PID controller
• IDM following model and Mobile lane-changing model

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Requirement

• Carla-0.9.12

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Run

1. run ./CarlaUE4.sh
2. run main_single_agent.py or multi_agent_agents.py

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Framework

1. Environment Configuration:

   • world_single_agent.py: Only ego car is controlled by the algorithm, and zombie cars are controlled by autopilot of carla.
   • world_multi_agents.py: All the car are controlled by the algorithm.

2. Perception:

   • feature.py extracts information from perception, including waypoints, lane points, traffic light, surrounding cars.

3. Trajectory Planning:

   • path_planner.py plan the path based on extracted waypoints
     • bezier_curve.py used in lane changing task
     • cubic_spline.py used in following task
   • velocity_planner.py formulates a quadratic program problem to optimize the quintic polynomial
     

4. Control:

   • pid.py: Throttle is calculated based on pid controller for velocity error, and steering is calculated based on pid controller for yaw and track error.

   • linear_mpc.py: The formulation of MPC is as follows:
     

     

     where $x_t=(p_{t,x},p_{t,y},v_t,\phi_t)$ , $u_t=(a_t, \theta_t)$, $du_t=u_t-u_t$. And $x_{ref}$ is obtained from path and velocity planner. $Q,R$ are the weights of MPC objective. The dynamic model is bicycle model that is defined in vehicle_model.py:

   
   

5. Rule-based decision:

   • rule_decision.py: please note the defined status for vehicles to complish follwing, lane changing and stopping tasks. The details of IDM and Mobile model can be seen in websites listed in reference.

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Reference

1. PythonRobotics
2. Intelligent-Driver Model
3. Lane-change Model MOBIL