# Safe planning and control under uncertainty for self-driving

arXiv: https://arxiv.org/abs/2010.11063 · HTML: https://mlsanity.com/paper/2010.11063/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 21 Oct 2020.

## Built on by (most cited first, 5 of 5)

- [Long-Tail Prediction Uncertainty Aware Trajectory Planning for Self-driving Vehicles](https://mlsanity.com/paper/2207.00788/) (2022)
- [Trajectory Planning for Autonomous Vehicle Using Iterative Reward Prediction in Reinforcement Learning](https://mlsanity.com/paper/2404.12079/) (2024)
- [Tube-Based Robust Control Strategy for Vision-Guided Autonomous Vehicles](https://mlsanity.com/paper/2503.18752/) (2025)
- [Robust Planning for Autonomous Driving via Mixed Adversarial Diffusion Predictions](https://mlsanity.com/paper/2505.12327/) (2025)
- [Uncertainty-Aware Vision-based Risk Object Identification via Conformal Risk Tube Prediction](https://mlsanity.com/paper/2603.23919/) (2026)

## Abstract (arXiv)

Motion Planning under uncertainty is critical for safe self-driving. In this paper, we propose a unified obstacle avoidance framework that deals with 1) uncertainty in ego-vehicle motion; and 2) prediction uncertainty of dynamic obstacles from environment. A two-stage traffic participant trajectory predictor comprising short-term and long-term prediction is used in the planning layer to generate safe but not over-conservative trajectories for the ego vehicle. The prediction module cooperates well with existing planning approaches. Our work showcases its effectiveness in a Frenet frame planner. A robust controller using tube MPC guarantees safe execution of the trajectory in the presence of state noise and dynamic model uncertainty. A Gaussian process regression model is used for online identification of the uncertainty's bound. We demonstrate effectiveness, safety, and real-time performance of our framework in the CARLA simulator.