With the promotion of policies and the continuous development of artificial intelligence, autonomous vehicle have gained more and more attention. The environment perception system is the key to realize the real-time interaction between the vehicle and the external environment. It is also the first step to realize automatic driving, and plays an important role in the safe driving of autonomous vehicle. The development and application of the environmental perception algorithms cannot be separated from testing and evaluation. The test evaluation can effectively verify the accuracy and stability of the environment perception algorithm, and provide reliable input for the decision-making and control of the auto drive system. This article establishes a perception algorithm testing system based on data playback, and proposes a testing and evaluation method for perception algorithms based on the environmental information requirements of decision control systems, in order to improve the development efficiency of environmental perception algorithms.
Currently, depth estimation of 2D image is widely treated as an important technology for environmental perception in autonomous driving, but it still suffers from many issues. From the view of application, this work proposes an unsupervised monocular depth estimation based on hybrid ViT to improve accuracy and reduce cost. Specifically, the technology of convolution and transformer have been combined in this work to encode to extract fine-grained features. Besides, fusing multi-scale features to decode is also adopted to generate multi-scale disparity maps. Then, the loss is calculated based on multi-scale and full-resolution disparity maps, and stereo constraints to realize image reconstruction have been achieved finally. Additionally, experiments have been carried out on the KITTI dataset, and the measured results indicate that compared with the previous works, this work has made progresses in the indicators of error and accuracy, i.e., higher accuracy of 3.4% than baseline, more clear boundaries, fewer artifacts and higher quality of depth maps. It is proved that the combination of hybrid encoding, multi-scale decoder and full-resolution loss can bring significant effect on depth estimation, especially the hybrid encoding.
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