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

Ying Shuai Quan, Paolo Falcone, Jonas Sjöberg

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.

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

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.
Paper Structure (36 sections, 40 equations, 3 figures, 2 tables)

This paper contains 36 sections, 40 equations, 3 figures, 2 tables.

Table of Contents

  1. Introduction
  2. Preliminaries
  3. Scenario Description
  4. EV Dynamics
  5. CLF Design for Lane Change
  6. Baseline ECBF Formulation
  7. Baseline joint EV--SV model
  8. ECBF Design
  9. RU ECBF
  10. Interaction-aware ECBF Design
  11. SV Dynamics via P-IDM
  12. Interaction-Aware Joint EV--SV Model
  13. IECBF Constraints for SV
  14. One-Step IECBF
  15. Predictive ECBF
  16. ...and 21 more sections

Figures (3)

  • Figure 1: Interactive versus nominal planning in an obstacle-avoidance lane-change scenario. Faded yellow and blue vehicles indicate predicted future positions of the SV and EV. The blue dotted curve shows interactive planning, where the SV is expected to decelerate and open a feasible gap for lane change. The red dotted curve shows nominal planning, where the SV is assumed to maintain constant velocity, making the maneuver infeasible.
  • Figure 2: EV control inputs $\delta_e$ and $a_e$ and CBF values generated by the baseline, nominal proposed, and robust proposed controllers.
  • Figure 3: Global trajectories for the baseline, nominal proposed, and robust proposed controllers. In all three plots, the SV is the black vehicle in the lower lane and the RU is the red circular obstacle.

Theorems & Definitions (2)

  • proof
  • proof