  To achieve fully autonomous driving, vehicles must be capable of continuously
performing various driving tasks, including lane keeping and car following,
both of which are fundamental and well-studied driving ones. However, previous
studies have mainly focused on individual tasks, and car following tasks have
typically relied on complete leader-follower information to attain optimal
performance. To address this limitation, we propose a vision-based deep
reinforcement learning (DRL) agent that can simultaneously perform lane keeping
and car following maneuvers. To evaluate the performance of our DRL agent, we
compare it with a baseline controller and use various performance metrics for
quantitative analysis. Furthermore, we conduct a real-world evaluation to
demonstrate the Sim2Real transfer capability of the trained DRL agent. To the
best of our knowledge, our vision-based car following and lane keeping agent
with Sim2Real transfer capability is the first of its kind.
