Edge-Assisted Collaborative Perception Against Jamming and Interference in Vehicular Networks

Collaborative perception of connected autonomous vehicles (CAVs) that offload the sensing data, such as the feature map extracted from light detection and ranging (LiDAR) point clouds, to an edge device such as the roadside unit (RSU) to detect traffic objects has severe performance degradation due to the offloading latency and packet loss rate (PLR) under jamming and interference. In this paper, we propose an edge-assisted reinforcement learning (RL)-based collaborative perception scheme for CAVs to enhance the accuracy and speed against jamming and interference in LiDAR-based object detection. Based on the spatial confidence score of the feature map, the data size, the channel gains, the received jamming power and interference level, this scheme chooses the critical regions of the feature map, radio channel and transmit power with the hierarchical structure to enhance the learning efficiency. The risk level of the selected policy evaluates the time asynchronization and information loss of the shared feature map using the multi-level risk function based on multiple thresholds of the offloading latency and PLR, with assigning different penalties to mitigate the selection of high-risk policies that degrade perception performance. The upper performance bound in terms of the perception accuracy, latency and utility is provided based on the Stackelberg equilibrium of the game between the jammer and CAVs. Experimental results based on the Robosense RS-LiDAR-16 sensors and the Raspberry Pi to detect 10 vehicles in an <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$8.5\times 4\times 3.5$ </tex-math></inline-formula> m<sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">3</sup> area show the performance gain with 22.4% higher perception accuracy and 41.3% less latency compared with the benchmark against a smart jammer.