In urban intersections, the sensory capabilities of autonomous vehicles (AVs) are often hindered by visual obstructions, posing significant challenges to their robust and safe operation. This paper presents an implementation study focused on enhancing the safety and robustness of Connected Automated Vehicles (CAVs) in scenarios with occluded visibility at urban intersections. A novel LiDAR Infrastructure System is established for roadside sensing, combined with Baidu Apollo’s Automated Driving System (ADS) and Cohda Wireless V2X communication hardware, and an integrated platform is established for roadside perception enhancement in autonomous driving. The field tests were conducted at the Singapore CETRAN (Centre of Excellence for Testing & Research of Autonomous Vehicles—NTU) autonomous vehicle test track, with the communication protocol adhering to SAE J2735 V2X communication standards. Communication latency and packet delivery ratio were analyzed as the evaluation metrics. The test results showed that the system can help CAV detect obstacles in advance under urban occluded scenarios.

Intelligent connected vehicles can perform better decision making and path planning tasks when they obtain the accurate motion state of preceding vehicles. In this article, an event-triggered estimation scheme considering the effects of packet loss and unknown vehicle inertia parameters is proposed for predicting the motion state of the preceding vehicle in vehicle-to-everything communications. First, an event-triggered communication mechanism is employed instead of the traditional periodic wireless communication rules to enable intermittent transmission of preceding vehicle sensor data to the host vehicle. Further based on the obtained sensor data, an event-triggered cubature Kalman filter (ETCKF) is designed for the host vehicle to estimate the motion state of the preceding vehicle. To further improve the robustness of the algorithm against perturbation of model parameters, a strong tracking algorithm is utilized to optimize the ETCKF to form a strong tracking ETCKF (STETCKF). Finally, the experimental results demonstrate that the estimation accuracy of the STETCKF under the communication rate of 7.08% and model parameters perturbation can be comparable to that of the cubature Kalman filter with 100% communication rate.