Due to its limited battery life and computing power, driverless cars are difficult to meet the processing needs of some delay-sensitive tasks or intensive tasks while ensuring battery life. To solve this problem, in the context of MEC, this paper proposed a driverless task offloading policy based on deep DQN. Firstly, this paper defined a "vehicle-edge-cloud" cooperative task offloading model based on task priority, which needed to jointly optimize the computing power of the vehicle and the task offloading policy to obtain the minimum delay and energy consumption of the system. Since the problem was a mixed-integer nonlinear programming problem and was NP-hard, this paper solved it in two steps—the first step obtained the analytical solution for the optimal computation power of vehicle through mathematical derivation, and then, under the fixed numerical value condition, the DQN algorithm obtained the optimal offloading strategy of the task. Finally, this paper established a simulation model by integrating tools such as SUMO, PyTorch and Python. This paper compared the DQN algorithm and the other three algorithms under different task loads, MEC server computation powers and energy consumption weight co-efficients. The experimental results verify the feasibility and superiority of the proposed policy.

[2022-04-15] Accepted Paper /doc/2022.02.0043/v1

[2022-09-05] Printed Article /doc/2022.02.0043/v2