Collaborative Perception Against Data Fabrication Attacks in Vehicular Networks

Collaborative perception in vehicular networks enables the connected autonomous vehicle (CAV) to gather sensing data, such as feature maps of light detection and ranging (LiDAR) point clouds, from neighboring CAVs to achieve higher perception accuracy, which has performance degradation against data fabrication attacks that share falsified sensing data with random probability. In this paper, we exploit the spatial consistency check to detect the potentially manipulated regions in LiDAR point clouds and measure the inconsistency degree of the received sensing data based on the number of conflict regions, which is the basis for determining the falsified sensing data if the inconsistency degree exceeds the threshold of the hypothesis test. The reinforcement learning (RL)-based collaborative vehicular perception scheme against data fabrication attacks is further proposed to choose CAVs based on the inconsistency degrees, the data quality measured by the confidence scores, the channel gains and the CAV reputations, which enhances the utility as the weighted sum of perception accuracy, speed and minimum latency requirement for data transmission. In addition, the multi-layer perceptron-based neural networks extract the perception features of sensing data from historical experiences, such as the data quality of received feature maps, as well as compress the RL state that linearly increases with the network scales and the spatial granularity of LiDAR point clouds for faster learning. Experimental results based on 10 CAVs equipped with LiDAR sensors and NVIDIA computational units to detect 20 vehicles against data fabrication attacks show that our proposed scheme outperforms the benchmarks in terms of perception accuracy and speed.