  With the development of state-of-art deep reinforcement learning, we can
efficiently tackle continuous control problems. But the deep reinforcement
learning method for continuous control is based on historical data, which would
make unpredicted decisions in unfamiliar scenarios. Combining deep
reinforcement learning and safety based control can get good performance for
self-driving and collision avoidance. In this passage, we use the Deep
Deterministic Policy Gradient algorithm to implement autonomous driving without
vehicles around. The vehicle can learn the driving policy in a stable and
familiar environment, which is efficient and reliable. Then we use the
artificial potential field to design collision avoidance algorithm with
vehicles around. The path tracking method is also taken into consideration. The
combination of deep reinforcement learning and safety based control performs
well in most scenarios.
