Interaction-Aware Predictive Environmental Control Barrier Function for Emergency Lane Change

TL;DR

This paper introduces an interaction-aware control barrier function framework for autonomous vehicle safety that predicts and incorporates surrounding vehicle reactions, enhancing safety and robustness in dense, interactive traffic scenarios.

Contribution

It develops a novel safety assessment method that predicts reactive vehicle behavior and accounts for uncertainties, improving over classical CBF approaches in interactive traffic environments.

Findings

Reduces conservativeness compared to classical CBF methods.

Enhances robustness to uncertainties in surrounding vehicle responses.

Provides more accurate safety assessments in dense traffic scenarios.

Abstract

Safety-critical motion planning in mixed traffic remains challenging for autonomous vehicles, especially when it involves interactions between the ego vehicle (EV) and surrounding vehicles (SVs). In dense traffic, the feasibility of a lane change depends strongly on how SVs respond to the EV motion. This paper presents an interaction-aware safety framework that incorporates such interactions into a control barrier function (CBF)-based safety assessment. The proposed method predicts near-future vehicle positions over a finite horizon, thereby capturing reactive SV behavior and embedding it into the CBF-based safety constraint. To address uncertainty in the SV response model, a robust extension is developed by treating the model mismatch as a bounded disturbance and incorporating an online uncertainty estimate into the barrier condition. Compared with classical environmental CBF methods that neglect SV reactions, the proposed approach provides a less conservative and more informative safety representation for interactive traffic scenarios, while improving robustness to uncertainty in the modeled SV behavior.