Engineered an advanced control system for an autonomous racing car in the CARLA simulation environment. Utilized Hybrid A* search for sophisticated path planning, enabling the car to navigate optimally through complex racetrack environments. Implemented a Proportional-Derivative (PD) controller, dynamically adjusting steering, speed, and braking for efficient real-time navigation. Enhanced the system by integrating obstacle avoidance capabilities into the path planning and control algorithms, further refining the car's autonomous operation.
https://www.youtube.com/watch?v=n1QGdyXPFZI
Installation Documentation could be found Here
- All you need to submit is
agent.py. And all you implementation should be contained in this file. - If you want to change to different map, just modify line 6 in
wrapper.py. 5 maps (shanghai_intl_circuit, t1_triple, t2_triple, t3, t4) have been made available to public, while we hold some hidden maps for testing. - If you would like to test your controller without the scenarios, comment out line 10 in
wrapper.py.
Benchmark score using our very naive controllers on below tracks with no scenarios:
| Track | Our Score (Hybrid A*) | Baseline Score (Yan's Score) | Percent Improvement |
|---|---|---|---|
| triple_t1 | 40 | 45 | 11.11% |
| t3 | 67.6 | 82 | 17.56% |
| t4 | 48 | 57 | 15.79% |
| shanghai_intl_circuit | 94.2 | 122 | 22.70% |
Benchmark score using our very naive controllers on below tracks with with scenarios:
| Track | Our Score (Hybrid A*) | Baseline Score (Yan's Score) | Percent Improvement |
|---|---|---|---|
| triple_t1 | 61.6 | 70 | 12.0% |
| t3 | 90.9 | 105 | 13.3% |
| t4 | 74.5 | 82 | 9.1% |
| shanghai_intl_circuit | 92.4 | 156 | 40.8% |