# Long Horizon Risk-Averse Motion Planning: a Model-Predictive Approach

arXiv: https://arxiv.org/abs/2209.07093 · HTML: https://mlsanity.com/paper/2209.07093/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 15 Sep 2022.

## Built on by (most cited first, 2 of 2)

- [SDS++: Online Situation-Aware Drivable Space Estimation for Automated Driving](https://mlsanity.com/paper/2406.01941/) (2024)
- [Towards Safer Autonomous Driving in an Open World: A Dual-Process Approach](https://mlsanity.com/paper/2610.04088/) (2026)

## Abstract (arXiv)

Developing safe automated vehicles that can be proactive, safe, and comfortable in mixed traffic requires improved planning methods that are risk-averse and that account for predictions of the motion of other road users. To consider these criteria, in this article, we propose a non-linear model-predictive trajectory generator scheme, which couples the longitudinal and lateral motion of the vehicle to steer the vehicle with minimal risk, while progressing towards the goal state. The proposed method takes into account the infrastructure, surrounding objects, and predictions of the objects' state through artificial potential-based risk fields included in the cost function of the model-predictive control (MPC) problem. This trajectory generator enables anticipatory maneuvers, i.e., mitigating risk far before any safety-critical intervention would be necessary. The method is proven in several case studies representing both highways- and urban situations. The results show the safe and efficient implementation of the MPC trajectory generator while proving its real-time applicability.