End-to-end Driving in High-Interaction Traffic Scenarios with Reinforcement Learning
<|reference_start|>End-to-end Driving in High-Interaction Traffic Scenarios with Reinforcement Learning: Dynamic and interactive traffic scenarios pose significant challenges for autonomous driving systems. Reinforcement learning (RL) offers a promising approach by enabling the exploration of driving policies beyond the constraints of pre-collected datasets and predefined conditions, particularly in complex environments. However, a critical challenge lies in effectively extracting spatial and temporal features from sequences of high-dimensional, multi-modal observations while minimizing the accumulation of errors over time. Additionally, efficiently guiding large-scale RL models to converge on optimal driving policies without frequent failures during the training process remains tricky.   We propose an end-to-end model-based RL algorithm named Ramble to address these issues. Ramble processes multi-view RGB images and LiDAR point clouds into low-dimensional latent features to capture the context of traffic scenarios at each time step. A transformer-based architecture is then employed to model temporal dependencies and predict future states. By learning a dynamics model of the environment, Ramble can foresee upcoming traffic events and make more informed, strategic decisions. Our implementation demonstrates that prior experience in feature extraction and decision-making plays a pivotal role in accelerating the convergence of RL models toward optimal driving policies. Ramble achieves state-of-the-art performance regarding route completion rate and driving score on the CARLA Leaderboard 2.0, showcasing its effectiveness in managing complex and dynamic traffic situations.<|reference_end|>
@article{li2024end-to-end,
    title={End-to-end Driving in High-Interaction Traffic Scenarios with
  Reinforcement Learning},
    author={Yueyuan Li, Mingyang Jiang, Songan Zhang, Wei Yuan, Chunxiang Wang,
  and Ming Yang},
    journal={arXiv preprint arXiv:2410.02253},
    year={2024},
    archivePrefix={arXiv},
    eprint={2410.02253},
    primaryClass={cs.AI cs.LG cs.RO}
}