Automotive perception involves understanding the external driving environment as well as the internal state of the vehicle cabin and occupants using sensor data. It is critical to achieving high levels of safety and autonomy in driving. This paper provides an overview of different sensor modalities like cameras, radars, and LiDARs used commonly for perception, along with the associated data processing techniques. Critical aspects in perception are considered, like architectures for processing data from single or multiple sensor modalities, sensor data processing algorithms and the role of machine learning techniques, methodologies for validating the performance of perception systems, and safety. The technical challenges for each aspect are analyzed, emphasizing machine learning approaches given their potential impact on improving perception. Finally, future research opportunities in automotive perception for their wider deployment are outlined.

Supervised object detection models based on deep learning technologies cannot perform well in domain shift scenarios where annotated data for training is always insufficient. To this end, domain adaptation technologies for knowledge transfer have emerged to handle the domain shift problems. A stepwise domain adaptive YOLO (S-DAYOLO) framework is developed which constructs an auxiliary domain to bridge the domain gap and uses a new domain adaptive YOLO (DAYOLO) in cross-domain object detection tasks. Different from the previous solutions, the auxiliary domain is composed of original source images and synthetic images that are translated from source images to the similar ones in the target domain. DAYOLO based on YOLOv5s is designed with a category-consistent regularization module and adaptation modules for image-level and instance-level features to generate domain invariant representations. Our proposed method is trained and evaluated by using five public driving datasets including Cityscapes, Foggy Cityscapes, BDD100K, KITTI, and KAIST. Experiment results demonstrate that object detection performance is significantly improved when using our proposed method in various domain shift scenarios for autonomous driving applications.