Analysis of a Modular Autonomous Driving Architecture: The Top Submission to CARLA Leaderboard 20 Challenge
<|reference_start|>Analysis of a Modular Autonomous Driving Architecture: The Top Submission to CARLA Leaderboard 20 Challenge: In this paper we present the architecture of the Kyber-E2E submission to the map track of CARLA Leaderboard 2.0 Autonomous Driving (AD) challenge 2023, which achieved first place. We employed a modular architecture for our solution consists of five main components: sensing, localization, perception, tracking/prediction, and planning/control. Our solution leverages state-of-the-art language-assisted perception models to help our planner perform more reliably in highly challenging traffic scenarios. We use open-source driving datasets in conjunction with Inverse Reinforcement Learning (IRL) to enhance the performance of our motion planner. We provide insight into our design choices and trade-offs made to achieve this solution. We also explore the impact of each component in the overall performance of our solution, with the intent of providing a guideline where allocation of resources can have the greatest impact.<|reference_end|>
@article{zhang2024analysis,
    title={Analysis of a Modular Autonomous Driving Architecture: The Top
  Submission to CARLA Leaderboard 2.0 Challenge},
    author={Weize Zhang, Mohammed Elmahgiubi, Kasra Rezaee, Behzad Khamidehi,
  Hamidreza Mirkhani, Fazel Arasteh, Chunlin Li, Muhammad Ahsan Kaleem, Eduardo
  R. Corral-Soto, Dhruv Sharma, and Tongtong Cao},
    journal={arXiv preprint arXiv:2405.01394},
    year={2024},
    archivePrefix={arXiv},
    eprint={2405.01394},
    primaryClass={cs.AI}
}