Human-Machine Shared Control Approach for the Takeover of CACC

TL;DR

This paper presents a shared control method for CACC that facilitates safe, smooth human takeover by modeling the interaction as a Stackelberg game, improving safety and traffic flow.

Contribution

It introduces a novel shared control approach that models human-machine interaction as a Stackelberg game, integrating human response into predictive control for CACC takeover.

Findings

Enables smooth CACC takeover maneuvers

Ensures string stability with less than 6 vehicles and limited human authority

Reduces upstream traffic impact by up to 60%

Abstract

Cooperative Adaptive Cruise Control (CACC) often requires human takeover for tasks such as exiting a freeway. Direct human takeover can pose significant risks, especially given the close-following strategy employed by CACC, which might cause drivers to feel unsafe and execute hard braking, potentially leading to collisions. This research aims to develop a CACC takeover controller that ensures a smooth transition from automated to human control. The proposed CACC takeover maneuver employs an indirect human-machine shared control approach, modeled as a Stackelberg competition where the machine acts as the leader and the human as the follower. The machine guides the human to respond in a manner that aligns with the machine's expectations, aiding in maintaining following stability. Additionally, the human reaction function is integrated into the machine's predictive control system, moving beyond a simple "prediction-planning" pipeline to enhance planning optimality. The controller has been verified to i) enable a smooth takeover maneuver of CACC; ii) ensure string stability in the condition that the platoon has less than 6 CAVs and human control authority is less than 40%; iii) enhance both perceived and actual safety through machine interventions; and iv) reduce the impact on upstream traffic by up to 60%.