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Object detection is an important visual content of the autonomous vehicle, the traditional detecting methods usually cost a lot of computational memory and elapsed time. This paper proposes to use lightweight deep convolutional neural network (MobilenetV3-SSDLite) to carry out the object detection task of autonomous vehicles. Simulation analysis based on this method is implemented, the feature layer obtained after h-swish activation function in the first Conv of the 13th bottleneck module in MobilenetV3 is taken as the first effective feature layer, and the feature layer before pooling and convolution of the antepenultimate layer in MobilenetV3 is taken as the second effective feature layer, and these two feature layers are extracted from the MobilenetV3 network.

In recent years, electric vehicle and hybrid vehicle are either on the market or under intensive research and development (R&D). Since the concept of auxiliary power unit (APU) was brought into the automotive industry, the range-extended electric vehicle (ReEV) has become the favor of the worldwide manufacturers. Normally, the APU starts and stops more frequently in response to the control strategy compared with traditional vehicles, which will affect the ride comfort of passengers. Thus, APU start-stop NVH refinement is an important aspect of ReEV R&D. In this paper, a subjective evaluation on a ReEV was performed to quickly diagnose NVH issues firstly. Based on subjective results, the NVH experiment in a semi-anechoic room was carried out to troubleshoot these issues. The accelerations of the APU mounts, the seat track and the steering wheel as well as interior noise level were acquired and analyzed.