  Tactical decision making and strategic motion planning for autonomous highway
driving are challenging due to the complication of predicting other road users'
behaviors, diversity of environments, and complexity of the traffic
interactions. This paper presents a novel end-to-end continuous deep
reinforcement learning approach towards autonomous cars' decision-making and
motion planning. For the first time, we define both states and action spaces on
the Frenet space to make the driving behavior less variant to the road
curvatures than the surrounding actors' dynamics and traffic interactions. The
agent receives time-series data of past trajectories of the surrounding
vehicles and applies convolutional neural networks along the time channels to
extract features in the backbone. The algorithm generates continuous
spatiotemporal trajectories on the Frenet frame for the feedback controller to
track. Extensive high-fidelity highway simulations on CARLA show the
superiority of the presented approach compared with commonly used baselines and
discrete reinforcement learning on various traffic scenarios. Furthermore, the
proposed method's advantage is confirmed with a more comprehensive performance
evaluation against 1000 randomly generated test scenarios.
