Validity Learning on Failures: Mitigating the Distribution Shift in Autonomous Vehicle Planning
<|reference_start|>Validity Learning on Failures: Mitigating the Distribution Shift in Autonomous Vehicle Planning: The planning problem constitutes a fundamental aspect of the autonomous driving framework. Recent strides in representation learning have empowered vehicles to comprehend their surrounding environments, thereby facilitating the integration of learning-based planning strategies. Among these approaches, Imitation Learning stands out due to its notable training efficiency. However, traditional Imitation Learning methodologies encounter challenges associated with the co-variate shift phenomenon. We propose Validity Learning on Failures, VL(on failure), as a remedy to address this issue. The essence of our method lies in deploying a pre-trained planner across diverse scenarios. Instances where the planner deviates from its immediate objectives, such as maintaining a safe distance from obstacles or adhering to traffic rules, are flagged as failures. The states corresponding to these failures are compiled into a new dataset, termed the failure dataset. Notably, the absence of expert annotations for this data precludes the applicability of standard imitation learning approaches. To facilitate learning from the closed-loop mistakes, we introduce the VL objective which aims to discern valid trajectories within the current environmental context. Experimental evaluations conducted on both reactive CARLA simulation and non-reactive log-replay simulations reveal substantial enhancements in closed-loop metrics such as \textit{Score, Progress}, and Success Rate, underscoring the effectiveness of the proposed methodology. Further evaluations against the Bench2Drive benchmark demonstrate that VL(on failure) outperforms the state-of-the-art methods by a large margin.<|reference_end|>
@article{arasteh2024validity,
    title={Validity Learning on Failures: Mitigating the Distribution Shift in
  Autonomous Vehicle Planning},
    author={Fazel Arasteh, Mohammed Elmahgiubi, Behzad Khamidehi, Hamidreza
  Mirkhani, Weize Zhang, Cao Tongtong, Kasra Rezaee},
    journal={arXiv preprint arXiv:2406.01544},
    year={2024},
    archivePrefix={arXiv},
    eprint={2406.01544},
    primaryClass={cs.RO cs.AI cs.LG}
}