Adaptive Advanced Emergency Braking on Combined Road Friction Coefficients

Şahin, Hasan · 2024 · DOAJ

DOI: 10.30939/ijastech..1348903

archive: archived pipeline: cataloged verified

Get this paper ↗ (DOI — opens at the source; we link to it, we don't host it)

Summary

This study addresses the limitations of standard Advanced Emergency Braking Systems (AEBS) when encountering sudden drops in road friction coefficients during braking. While AEBS effectively prevents rear-end collisions on stable surfaces, a rapid decrease in friction (e.g., transitioning from dry to wet asphalt) significantly reduces braking efficiency and Time to Collision (TTC). The research proposes an adaptive AEBS that integrates autonomous emergency steering maneuvers with full braking to maintain vehicle stability and avoid collisions under these dynamic conditions. The motivation stems from the challenge of performing lateral maneuvers while longitudinal tire forces are maximized during hard braking, a scenario where human drivers may lack the necessary steering speed and precision. The methodology employs a predictive controller designed in MATLAB/Simulink using a linearized one-track vehicle model, verified against a full four-wheel vehicle model in the IPG/CarMaker simulation environment. The vehicle model, based on a Jaguar XJ series, includes non-linear tire parameters and load transfer effects. The controller utilizes Model Predictive Control (MPC) to determine optimal front-wheel steering angles based on yaw rate. Controller weighting factors were calibrated using Quantum Particle Optimization (QPO) to minimize a fitness function derived from yaw rate and steering angle limits. The simulation scenario involves a host vehicle cruising at 100 km/h approaching a stationary obstacle, with the road friction coefficient suddenly decreasing from dry to wet conditions during the braking maneuver. Results from three simulation cases demonstrate the efficacy of the proposed system. In the first case, standard AEBS successfully avoided a collision on dry asphalt. In the second case, standard AEBS failed to avoid a collision when friction dropped to wet conditions, resulting in a crash at approximately 15.25 seconds. In the third case, the adaptive AEBS successfully avoided the collision by initiating an emergency steering maneuver at 13.75 seconds. The system maintained lateral stability with a maximum lateral acceleration of 0.5g, consistent with wet asphalt limits. The adaptive system achieved steering wheel angle rates that exceeded human capability, highlighting the necessity of autonomous intervention. The predictive controller accurately calculated instantaneous road friction by observing longitudinal and normal tire forces, allowing for timely steering adjustments. The significance of this work lies in demonstrating that combining longitudinal braking with lateral steering control can prevent collisions in scenarios where braking alone is insufficient due to changing road conditions. The study concludes that adaptive AEBS, supported by predictive controllers, offers a robust solution for maintaining vehicle stability and safety during critical emergency maneuvers. However, the authors note that the current simulation assumes the target lane is clear, identifying Blind Spot Detection integration and energy consumption analysis as necessary areas for future research. This approach advances the field of Advanced Driver Assistance Systems by addressing the complex interplay between longitudinal and lateral dynamics under varying friction conditions.

Provenance

The full processing record for this entry. Every stage of this paper's journey through the pipeline is logged — what ran, with which tool and model, how many attempts it took, and when it last completed.

Summary generated by qwen3.6-27b-prismaquant on 2026-06-26; verification: verified.

Topics

Ranked by relevance to this paper. Hover a topic for its definition.

• adas effectiveness