arXiv research

A new car following model reduces platoon level traffic errors.

This study proposes a framework for human-like autonomous car-following planning based on deep reinforcement learning (deep RL). Historical driving data are fed into a simulation environment where an RL agent learns from trial and error interactions based on a reward function that signals how much the agent deviates fr…

A new model captures car-following and lane-changing behaviors in traffic.

A model used for velocity control during car following was proposed based on deep reinforcement learning (RL). To fulfil the multi-objectives of car following, a reward function reflecting driving safety, efficiency, and comfort was constructed. With the reward function, the RL agent learns to control vehicle speed in …

Compute and memory constraints have historically prevented traffic simulation software users from fully utilizing the predictive models underlying them. When calibrating car-following models, particularly, accommodations have included 1) using sensitivity analysis to limit the number of parameters to be calibrated, and…

A novel observer-based method detects and recovers anomalies in CAV sensor readings.

Bayesian approach improves car-following model calibration and validation with limited data.

Adaptive vehicle trajectory prediction for safer autonomous driving.

Reinforcement Learning (RL) algorithms have found limited success beyond simulated applications, and one main reason is the absence of safety guarantees during the learning process. Real world systems would realistically fail or break before an optimal controller can be learned. To address this issue, we propose a cont…

This paper surveys DRL for autonomous vehicle motion planning.

Unified approach detects traffic conflicts across various interactions.

This paper improves transportation efficiency by teaching automated vehicles to cooperate.