# Optimal Vehicle Path Planning Using Quadratic Optimization for Baidu Apollo Open Platform

arXiv: https://arxiv.org/abs/2112.02132 · HTML: https://mlsanity.com/paper/2112.02132/

## Key facts

- Cited by 6 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 3 Dec 2021.

## Built on by (most cited first, 6 of 6)

- [Differentiable Integrated Motion Prediction and Planning with Learnable Cost Function for Autonomous Driving](https://mlsanity.com/paper/2207.10422/) (2022)
- [Learning Online Belief Prediction for Efficient POMDP Planning in Autonomous Driving](https://mlsanity.com/paper/2401.15315/) (2024)
- [Decictor: Towards Evaluating the Robustness of Decision-Making in Autonomous Driving Systems](https://mlsanity.com/paper/2402.18393/) (2024)
- [Designing a Recurrent Neural Network to Learn a Motion Planner for High-Dimensional Inputs](https://mlsanity.com/paper/2205.04799/) (2022)
- [Human-Like Implicit Intention Expression for Autonomous Driving Motion Planning: A Method Based on Learning Human Intention Priors](https://mlsanity.com/paper/2307.15950/) (2023)
- [STAGE: STyle-controllable Action GEneration for personalized autonomous driving](https://mlsanity.com/paper/2607.29517/) (2026)

## Abstract (arXiv)

Path planning is a key component in motion planning for autonomous vehicles. A path specifies the geometrical shape that the vehicle will travel, thus, it is critical to safe and comfortable vehicle motions. For urban driving scenarios, autonomous vehicles need the ability to navigate in cluttered environment, e.g., roads partially blocked by a number of vehicles/obstacles on the sides. How to generate a kinematically feasible and smooth path, that can avoid collision in complex environment, makes path planning a challenging problem. In this paper, we present a novel quadratic programming approach that generates optimal paths with resolution-complete collision avoidance capability.