Improving Ride Comfort in Automated Vehicles Using V2I Communication at Intersections

As vehicle automation continues to progress to-wards full autonomy, the potential for increasingly sophisticated interactions between vehicles and traffic infrastructure presents new opportunities and challenges for improving the driving experience. One critical area is passenger comfort, particularly during transitions at signalized intersections. Vehicles that rely solely on onboard perception may respond abruptly to traffic signal changes, leading to uncomfortable acceleration or deceleration patterns. By leveraging vehicle-to-infrastructure (V2I) communication, automated systems could react in anticipation of signal transitions to enhance ride comfort and overall system efficiency. This study explores the potential benefits of V2I communication in enhancing passenger comfort at signalized intersections. Two operating scenarios are compared: one that relies exclusively on the car perception system, and another that incorporates V2I communication, which provides traffic signal timing data to the automated vehicle (AV). A co-simulation testbed integrating CARLA, Robot Operating System 2 (ROS 2), ns-3, and MATLAB/Simulink is developed to model both scenarios and assess passenger comfort based on acceleration and deceleration metrics. The results show that V2I-enabled AVs achieve smoother and more predictable deceleration, reducing abrupt maneuvers and enhancing ride comfort without compromising safety. By quantifying these improvements, this work contributes to a better understanding of how V2I communication can enhance passenger experience while supporting safer and more anticipatory driving behaviors.