# Safe and Non-Conservative Trajectory Planning for Autonomous Driving Handling Unanticipated Behaviors of Traffic Participants

arXiv: https://arxiv.org/abs/2406.13396 · HTML: https://mlsanity.com/paper/2406.13396/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Placed in Reinforcement learning for driving, one of 9 directions in End-to-end autonomous driving.
- Builds on 6 of its 25 references that are in the map.
- First posted on arXiv 19 Jun 2024.

## Placed in

- End-to-end autonomous driving / [Reinforcement learning for driving](https://mlsanity.com/driving/reinforcement-learning-for-driving/)

## Builds on (most cited first, 6 of 6)

- [CVXPY: A Python-Embedded Modeling Language for Convex Optimization](https://mlsanity.com/paper/1603.00943/) (2016)
- [Stochastic Model Predictive Control with a Safety Guarantee for Automated Driving: Extended Version](https://mlsanity.com/paper/2009.09381/) (2020)
- [Autonomous Racing using Learning Model Predictive Control](https://mlsanity.com/paper/1610.06534/) (2016)
- [Contingency Model Predictive Control for Automated Vehicles](https://mlsanity.com/paper/1903.08818/) (2019)
- [Minimization of Constraint Violation Probability in Model Predictive Control](https://mlsanity.com/paper/2006.02337/) (2020)
- [Minimal Constraint Violation Probability in Model Predictive Control for Linear Systems](https://mlsanity.com/paper/2402.10538/) (2024)

## Built on by (most cited first, 1 of 1)

- [Safe and Nonconservative Contingency Planning for Autonomous Vehicles via Online Learning-Based Reachable Set Barriers](https://mlsanity.com/paper/2509.07464/) (2025)

## Abstract (arXiv)

Trajectory planning for autonomous driving is challenging because the unknown future motion of traffic participants must be accounted for, yielding large uncertainty. Stochastic Model Predictive Control (SMPC)-based planners provide non-conservative planning, but do not rule out a (small) probability of collision. We propose a control scheme that yields an efficient trajectory based on SMPC when the traffic scenario allows, still avoiding that the vehicle causes collisions with traffic participants if the latter move according to the prediction assumptions. If some traffic participant does not behave as anticipated, no safety guarantee can be given. Then, our approach yields a trajectory which minimizes the probability of collision, using Constraint Violation Probability Minimization techniques. Our algorithm can also be adapted to minimize the anticipated harm caused by a collision. We provide a thorough discussion of the benefits of our novel control scheme and compare it to a previous approach through numerical simulations from the CommonRoad database.