Measurement Framework for Quantifying Braking Comfort in Automated Vehicles

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Abstract

Passenger comfort during braking remains a critical challenge in automated vehicles, as abrupt deceleration and motion transitions can degrade ride quality and user acceptance. Existing evaluation approaches often rely on isolated peak or aggregate metrics, which do not capture the time-evolving nature of braking maneuvers or provide a consistent basis for comparison across operating conditions. This paper develops a measurement-driven framework to quantify braking comfort using objective metrics derived from vehicle dynamics. A controlled experimental methodology is implemented to collect synchronized data across a structured set of distance and speed conditions using an instrumented automated vehicle. The framework integrates longitudinal acceleration, jerk, and braking duration within a unified distance-based representation, enabling consistent characterization of braking behavior. Experimental results show that braking follows a distance-constrained regime, where the available stopping distance governs both motion smoothness and passenger comfort. Reduced stopping distance leads to higher acceleration magnitude, increased jerk, and shorter braking duration, while extended distance enables smoother and more gradual deceleration profiles. These findings provide a quantitative foundation for evaluating communication-assisted braking. By extending effective stopping distance through Vehicle-to-Infrastructure (V2I) and Vehicle-to-Network (V2N) communication, earlier awareness of downstream conditions can improve braking smoothness. The proposed framework and dataset support future integration with simulation and connected vehicle systems for assessing the impact of information availability on driving performance.

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Automotive

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