# Task-Motion Planning for Safe and Efficient Urban Driving

arXiv: https://arxiv.org/abs/2003.03807 · HTML: https://mlsanity.com/paper/2003.03807/

## Key facts

- Cited by 7 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 8 Mar 2020.

## Built on by (most cited first, 7 of 7)

- [LLM+P: Empowering Large Language Models with Optimal Planning Proficiency](https://mlsanity.com/paper/2304.11477/) (2023)
- [NATURAL PLAN: Benchmarking LLMs on Natural Language Planning](https://mlsanity.com/paper/2406.04520/) (2024)
- [ISR-LLM: Iterative Self-Refined Large Language Model for Long-Horizon Sequential Task Planning](https://mlsanity.com/paper/2308.13724/) (2023)
- [Grounding Classical Task Planners via Vision-Language Models](https://mlsanity.com/paper/2304.08587/) (2023)
- [DKPROMPT: Domain Knowledge Prompting Vision-Language Models for Open-World Planning](https://mlsanity.com/paper/2406.17659/) (2024)
- [LLM+MAP: Bimanual Robot Task Planning using Large Language Models and Planning Domain Definition Language](https://mlsanity.com/paper/2503.17309/) (2025)
- [RoboGPT: an intelligent agent of making embodied long-term decisions for daily instruction tasks](https://mlsanity.com/paper/2311.15649/) (2023)

## Abstract (arXiv)

Autonomous vehicles need to plan at the task level to compute a sequence of symbolic actions, such as merging left and turning right, to fulfill people's service requests, where efficiency is the main concern. At the same time, the vehicles must compute continuous trajectories to perform actions at the motion level, where safety is the most important. Task-motion planning in autonomous driving faces the problem of maximizing task-level efficiency while ensuring motion-level safety. To this end, we develop algorithm Task-Motion Planning for Urban Driving (TMPUD) that, for the first time, enables the task and motion planners to communicate about the safety level of driving behaviors. TMPUD has been evaluated using a realistic urban driving simulation platform. Results suggest that TMPUD performs significantly better than competitive baselines from the literature in efficiency, while ensuring the safety of driving behaviors.