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

TL;DR

This paper introduces a risk-averse, non-linear model-predictive control method for vehicle motion planning that anticipates and mitigates risks in complex traffic scenarios, ensuring safety and efficiency.

Contribution

It presents a novel MPC-based trajectory generator that integrates risk fields and vehicle dynamics for proactive, safe maneuvering in mixed traffic environments.

Findings

Effective in highway and urban scenarios

Ensures real-time safe trajectory planning

Demonstrates anticipatory risk mitigation

Abstract

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.