Learning Multiple Probabilistic Decisions from Latent World Model in Autonomous Driving
<|reference_start|>Learning Multiple Probabilistic Decisions from Latent World Model in Autonomous Driving: The autoregressive world model exhibits robust generalization capabilities in vectorized scene understanding but encounters difficulties in deriving actions due to insufficient uncertainty modeling and self-delusion. In this paper, we explore the feasibility of deriving decisions from an autoregressive world model by addressing these challenges through the formulation of multiple probabilistic hypotheses. We propose LatentDriver, a framework models the environment's next states and the ego vehicle's possible actions as a mixture distribution, from which a deterministic control signal is then derived. By incorporating mixture modeling, the stochastic nature of decisionmaking is captured. Additionally, the self-delusion problem is mitigated by providing intermediate actions sampled from a distribution to the world model. Experimental results on the recently released close-loop benchmark Waymax demonstrate that LatentDriver surpasses state-of-the-art reinforcement learning and imitation learning methods, achieving expert-level performance. The code and models will be made available at https://github.com/Sephirex-X/LatentDriver.<|reference_end|>
@article{xiao2024learning,
    title={Learning Multiple Probabilistic Decisions from Latent World Model in
  Autonomous Driving},
    author={Lingyu Xiao, Jiang-Jiang Liu, Sen Yang, Xiaofan Li, Xiaoqing Ye,
  Wankou Yang and Jingdong Wang},
    journal={arXiv preprint arXiv:2409.15730},
    year={2024},
    archivePrefix={arXiv},
    eprint={2409.15730},
    primaryClass={cs.RO cs.AI}
}