# TDR-OBCA: A Reliable Planner for Autonomous Driving in Free-Space Environment

arXiv: https://arxiv.org/abs/2009.11345 · HTML: https://mlsanity.com/paper/2009.11345/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 23 Sep 2020.

## Built on by (most cited first, 4 of 4)

- [Real-Time Trajectory Planning for Autonomous Driving with Gaussian Process and Incremental Refinement](https://mlsanity.com/paper/2205.11853/) (2022)
- [HOPE: A Reinforcement Learning-based Hybrid Policy Path Planner for Diverse Parking Scenarios](https://mlsanity.com/paper/2405.20579/) (2024)
- [Motion Comfort Optimization for Autonomous Vehicles: Concepts, Methods, and Techniques](https://mlsanity.com/paper/2306.09462/) (2023)
- [IA Planner: Motion Planning Using Instantaneous Analysis for Autonomous Vehicle in the Dense Dynamic Scenarios on Highways](https://mlsanity.com/paper/2103.10909/) (2021)

## Abstract (arXiv)

This paper presents an optimization-based collision avoidance trajectory generation method for autonomous driving in free-space environments, with enhanced robustness, driving comfort and efficiency. Starting from the hybrid optimization-based framework, we introduces two warm start methods, temporal and dual variable warm starts, to improve the efficiency. We also reformulate the problem to improve the robustness and efficiency. We name this new algorithm TDR-OBCA. With these changes, compared with original hybrid optimization we achieve a 96.67% failure rate decrease with respect to initial conditions, 13.53% increase in driving comforts and 3.33% to 44.82% increase in planner efficiency as obstacles number scales. We validate our results in hundreds of simulation scenarios and hundreds of hours of public road tests in both U.S. and China. Our source code is available at https://github.com/ApolloAuto/apollo.