Interaction-aware Traffic Prediction and Scenario-based Model Predictive Control for Autonomous Vehicles on Highways

TL;DR

This paper presents an integrated framework combining traffic prediction and scenario-based model predictive control for autonomous vehicles on highways, emphasizing safety, comfort, and adherence to traffic rules.

Contribution

It introduces a novel scenario-based control system that incorporates interaction-aware traffic prediction and guarantees recursive feasibility for safe autonomous driving.

Findings

The proposed controller ensures safe maneuvers in various traffic scenarios.

Simulation results demonstrate improved safety and comfort.

The approach effectively predicts traffic behavior considering interactions.

Abstract

This paper addresses the problem of traffic prediction and control of autonomous vehicles on highways. A modified Interacting Multiple Model Kalman filter algorithm is applied to predict the motion behavior of the traffic participants by considering their interactions. A scenario generation component is used to produce plausible scenarios of the vehicles based on the predicted information. A novel integrated decision-making and control system is proposed by applying a Scenario-based Model Predictive Control approach. The designed controller considers safety, driving comfort, and traffic rules. The recursive feasibility of the controller is guaranteed under the inclusion of the `worst case' as an additional scenario to obtain safe inputs. Finally, the proposed scheme is evaluated using the HighD dataset. Simulation results indicate that the vehicle performs safe maneuvers in different traffic situations under the designed control framework.