  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 a fashion that maximizes cumulative rewards, through trials
and errors in the simulation environment. A total of 1,341 car-following events
extracted from the Next Generation Simulation (NGSIM) dataset were used to
train the model. Car-following behavior produced by the model were compared
with that observed in the empirical NGSIM data, to demonstrate the model's
ability to follow a lead vehicle safely, efficiently, and comfortably. Results
show that the model demonstrates the capability of safe, efficient, and
comfortable velocity control in that it 1) has small percentages (8\%) of
dangerous minimum time to collision values (\textless\ 5s) than human drivers
in the NGSIM data (35\%); 2) can maintain efficient and safe headways in the
range of 1s to 2s; and 3) can follow the lead vehicle comfortably with smooth
acceleration. The results indicate that reinforcement learning methods could
contribute to the development of autonomous driving systems.

Machine Learning;Artificial Intelligence;Robotics;Machine Learning