The in-vehicle controller area network (CAN) bus is one of the essential components for autonomous vehicles, and its safety will be one of the greatest challenges in the field of intelligent vehicles in the future. In this paper, we propose a novel system that uses a deep neural network (DNN) to detect anomalous CAN bus messages. We treat anomaly detection as a cross-domain modelling problem, in which three CAN bus data packets as a group are directly imported into the DNN architecture for parallel training with shared weights. After that, three data packets are represented as three independent feature vectors, which corresponds to three different types of data sequences, namely anchor, positive and negative. The proposed DNN architecture is an embedded triplet loss network that optimizes the distance between the anchor example and the positive example, makes it smaller than the distance between the anchor example and the negative example, and realizes the similarity calculation of samples, which were originally used in face detection. Compared to traditional anomaly detection methods, the proposed method to learn the parameters with shared-weight could improve detection efficiency and detection accuracy. The whole detection system is composed of the front-end and the back-end, which correspond to deep network and triplet loss network, respectively, and are trainable in an end-to-end fashion. Experimental results demonstrate that the proposed technology can make real-time responses to anomalies and attacks to the CAN bus, and significantly improve the detection ratio. To the best of our knowledge, the proposed method is the first used for anomaly detection in the in-vehicle CAN bus.

Path planning is a significant and challenging task in the domain of automatic driving. Many applications, such as autonomous driving, robotic navigation and aircraft object tracking in complex and changing urban road scenes, need accurate and robust path planning by detecting obstacles in the forward direction. The traditional methods only rely on the path search method without considering the environmental factors, the vehicle path planning method cannot deal with the complex and changeable environment. To deal with above problems, we propose a perception-aware based multi-modal feature fusion approach that combines visual-inertial odometer (VIO) poses and semantic obstacles in the forward scene of vehicles to plan driving paths. The proposed method takes environment awareness as the guide and combines path search algorithm to realize path optimization task in complex environment. The proposed approach first uses a long short memory network (LSTM) to build a VIO that fuses visual and inertial data for pose estimation. To detect obstacles, the proposed method uses a segmentation model with a lightweight structure to extract semantic 3D landmarks. Finally, a path search strategy combining an A* algorithm and visual information is proposed to plan driving paths for intelligent vehicles. We estimate the proposed path planning method on assimilated scenes and public datasets (KITTI and Cityscapes) by using a micro controller (Jetson Xavier NX) installed on a small vehicle. We also show comparable results with path planning that only uses the greedy algorithm or heuristic algorithm without using visual information and show that our method is adequate in coping with different complex scenes.