Automated Lane Change via Adaptive Interactive MPC: Human-in-the-Loop Experiments

TL;DR

This paper introduces aiMPC, an adaptive interactive MPC algorithm for autonomous vehicles that optimizes lane change maneuvers in human-in-the-loop scenarios, improving mobility and safety.

Contribution

The paper presents a novel adaptive interactive mixed-integer MPC algorithm that incorporates human driver behavior via inverse optimal control for autonomous vehicle planning.

Findings

Enhanced vehicle mobility compared to baseline methods

Effective handling of non-convex constraints and lane discipline

Successful human-in-the-loop testing in simulated scenarios

Abstract

This article presents a new optimal control-based interactive motion planning algorithm for an autonomous vehicle interacting with a human-driven vehicle. The ego vehicle solves a joint optimization problem for its motion planning involving costs and coupled constraints of both vehicles and applies its own actions. The non-convex feasible region and lane discipline are handled by introducing integer decision variables and the resulting optimization problem is a mixed-integer quadratic program (MIQP) which is implemented via model predictive control (MPC). Furthermore, the ego vehicle imputes the cost of human-driven neighboring vehicle (NV) using an inverse optimal control method based on Karush-Kuhn-Tucker (KKT) conditions and adapts the joint optimization cost accordingly. We call the algorithm adaptive interactive mixed-integer MPC (aiMPC). Its interaction with human subjects driving the NV in a mandatory lane change scenario is tested in a developed software-and-human-in-the-loop simulator. Results show the effectiveness of the presented algorithm in terms of enhanced mobility of both the vehicles compared to baseline methods.