基于 AUTOSAR 的汽车控制器软件优化部署研究

In order to deal with the optimal deployment of software from SW-C (SoftWare-Component) to ECU (Electric Control Unit), from Runnable to OsTask (Operation System Task) and from OsTask to Core in multicore ECU in the software development process for AUTOSAR-based automotive controller, AUTOSAR-based software topology and optimal deployment model of automotive controller were constructed for practical engineering application requirements. Firstly, an improved SAC (Soft Actor-Critic) deep reinforcement learning solver framework was proposed based on D2RL(deep dense architecture in reinforcement learning) and PER(prioritized experience replay). And then some simulation experiments were carried out to demonstrate the proposed method. Results show the superior performance and stability of the new method, compared with commonly used heuristic algorithms in terms of ECU core load balancing, OsTask stack space utilization, as well as the utilization of communication bandwidth between ECUs and among cores.