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Real-world autonomous driving (AD) like urban driving involves many corner cases. The lately released AD Benchmark CARLA Leaderboard v2 (a.k.a. CARLA v2) involves 39 new common events in the driving scene, providing a more quasi-realistic testbed compared to CARLA Leaderboard v1. It poses new challenges and so far no literature has reported any success on the new scenarios in v2. In this work, we take the initiative of directly training a neural planner and the hope is to handle the corner cases flexibly and effectively. To our best knowledge, we develop the first model-based RL method (named Think2Drive) for AD, with a compact latent world model to learn the transitions of the environment, and then it acts as a neural simulator to train the agent i.e. planner. It significantly boosts the training efficiency of RL thanks to the low dimensional state space and parallel computing of tensors in the latent world model. Think2Drive is able to run in an expert-level proficiency in CARLA v2 within 3 days of training on a single A6000 GPU, and to our best knowledge, so far there is no reported success (100% route completion) on CARLA v2. We also develop CornerCaseRepo, a benchmark that supports the evaluation of driving models by scenarios. We also propose a balanced metric to evaluate the performance by route completion, infraction number, and scenario density.

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Think2Drive: Efficient Reinforcement Learning by Thinking with Latent World Model for Autonomous Driving (in CARLA-V2)

  • 会议论文

  • 发表日期：

    2025年

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  • Qifeng Li,
  • Xiaosong Jia,
  • Shaobo Wang,
  • Junchi Yan

摘要

Real-world autonomous driving (AD) like urban driving involves many corner cases. The lately released AD Benchmark CARLA Leaderboard v2 (a.k.a. CARLA v2) involves 39 new common events in the driving scene, providing a more quasi-realistic testbed compared to CARLA Leaderboard v1. It poses new challenges and so far no literature has reported any success on the new scenarios in v2. In this work, we take the initiative of directly training a neural planner and the hope is to handle the corner cases flexibly and effectively. To our best knowledge, we develop the first model-based RL method (named Think2Drive) for AD, with a compact latent world model to learn the transitions of the environment, and then it acts as a neural simulator to train the agent i.e. planner. It significantly boosts the training efficiency of RL thanks to the low dimensional state space and parallel computing of tensors in the latent world model. Think2Drive is able to run in an expert-level proficiency in CARLA v2 within 3 days of training on a single A6000 GPU, and to our best knowledge, so far there is no reported success (100% route completion) on CARLA v2. We also develop CornerCaseRepo, a benchmark that supports the evaluation of driving models by scenarios. We also propose a balanced metric to evaluate the performance by route completion, infraction number, and scenario density.