# LeTS-Drive: Driving in a Crowd by Learning from Tree Search

arXiv: https://arxiv.org/abs/1905.12197 · HTML: https://mlsanity.com/paper/1905.12197/

## Key facts

- Cited by 13 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 29 May 2019.

## Built on by (most cited first, 13 of 13)

- [Large Language Models as Commonsense Knowledge for Large-Scale Task Planning](https://mlsanity.com/paper/2305.14078/) (2023)
- [Reward (Mis)design for Autonomous Driving](https://mlsanity.com/paper/2104.13906/) (2021)
- [INVIGORATE: Interactive Visual Grounding and Grasping in Clutter](https://mlsanity.com/paper/2108.11092/) (2021)
- [A Review of Reward Functions for Reinforcement Learning in the context of Autonomous Driving](https://mlsanity.com/paper/2405.01440/) (2024)
- [Closing the Planning-Learning Loop with Application to Autonomous Driving](https://mlsanity.com/paper/2101.03834/) (2021)
- [REGRAD: A Large-Scale Relational Grasp Dataset for Safe and Object-Specific Robotic Grasping in Clutter](https://mlsanity.com/paper/2104.14118/) (2021)
- [LEADER: Learning Attention over Driving Behaviors for Planning under Uncertainty](https://mlsanity.com/paper/2209.11422/) (2022)
- [Deep Visual Navigation under Partial Observability](https://mlsanity.com/paper/2109.07752/) (2021)
- [Learning Online Belief Prediction for Efficient POMDP Planning in Autonomous Driving](https://mlsanity.com/paper/2401.15315/) (2024)
- [Hi-Drive: Hierarchical POMDP Planning for Safe Autonomous Driving in Diverse Urban Environments](https://mlsanity.com/paper/2409.18411/) (2024)
- [Measurable Monte Carlo Search Error Bounds](https://mlsanity.com/paper/2106.04715/) (2021)
- [Balancing Progress and Safety: A Novel Risk-Aware Objective for RL in Autonomous Driving](https://mlsanity.com/paper/2505.06737/) (2025)
- [Zero-shot Transfer Learning of Driving Policy via Socially Adversarial Traffic Flow](https://mlsanity.com/paper/2304.12821/) (2023)

## Abstract (arXiv)

Autonomous driving in a crowded environment, e.g., a busy traffic intersection, is an unsolved challenge for robotics. The robot vehicle must contend with a dynamic and partially observable environment, noisy sensors, and many agents. A principled approach is to formalize it as a Partially Observable Markov Decision Process (POMDP) and solve it through online belief-tree search. To handle a large crowd and achieve real-time performance in this very challenging setting, we propose LeTS-Drive, which integrates online POMDP planning and deep learning. It consists of two phases. In the offline phase, we learn a policy and the corresponding value function by imitating the belief tree search. In the online phase, the learned policy and value function guide the belief tree search. LeTS-Drive leverages the robustness of planning and the runtime efficiency of learning to enhance the performance of both. Experimental results in simulation show that LeTS-Drive outperforms either planning or imitation learning alone and develops sophisticated driving skills.