Infrastructure-based cooperative perception has been researched for several years, but few automotive warning or control applications using this information have been published. Infrastructure sensing, such as with cameras or lidars, and a communication system, allows connected vehicles to receive information about all observed objects. An SAE standard, “V2X Sensor-Sharing for Cooperative and Automated Driving” (J3224), released in 2022, introduces the Sensor Data Sharing Message (SDSM) as the standard communication message for cooperative perception. This paper investigates the use of the SDSM for a vehicle application to provide warnings of potential collisions with vulnerable road users who will cross the street at the intersection. The application was tested in CARLA simulation under various roadside detection errors and communication conditions to assess the impact on the on-board application and estimate the minimum detection and communication requirements for effective use. In addition, the system was implemented and evaluated at the Mcity test facility. The results demonstrate that the proposed warning system can accurately and promptly warn the driver, given specific communication conditions, and show that the SDSM is viable for real-time on-board usage.

Roadside perception has become a critical component for connected and automated vehicles (CAVs), enhancing safety and offering a comprehensive view of the traffic environment that onboard detection systems alone cannot provide. By supplementing the limitations of onboard sensors, roadside perception systems improve the accuracy and reliability of detecting and localizing vehicles and pedestrians in challenging locations. Currently, a variety of cameras, including fisheye and regular cameras, are deployed along roadsides for surveillance purposes. These sensors have significant potential to improve vehicle and pedestrian detection. This paper extends our previous work on single image sensor vehicle detection by developing a comprehensive multiple sensor fusion framework. We take advantage of the complementary strengths of multiple fisheye and regular cameras to enhance the accuracy and robustness of the perception system. The proposed system has been extensively tested in Mcity, a controlled urban testing environment, through numerous field tests. The results demonstrate the effectiveness of our approach, showcasing promising improvements in vehicle and pedestrian detection and tracking accuracy.

In recent years, there has been growing interest in heterogeneous distributed storage systems (DSSs), such as clustered DSSs, which are widely used in practice. However, research regarding information-theoretic security in heterogeneous DSSs remains limited. Furthermore, unlike traditional DSSs, the heterogeneous DSSs face eavesdropper with diverse operating patterns, complicating the secrecy models. In this paper, we aim to investigate the secrecy capacity and code constructions for clustered DSSs (CDSSs), a type of heterogeneous DSSs in which the system is divided into clusters with an equal number of nodes and different repair bandwidths for intra-cluster and cross-cluster against two types of eavesdroppers: the occupying-type eavesdropper and the osmotic-type eavesdropper. We construct two CDSS secrecy models tailored to these aforementioned eavesdroppers, derive the upper bounds on adjustable secrecy capacities, and explore the relationships between the upper bounds of perfect secrecy capacities and the number of compromised nodes. Notably, the upper bounds obtained in this paper generalize those of the traditional DSS model. Additionally, we propose three repair-by-transfer code constructions that achieve the secrecy capacity under both eavesdropper scenarios. These codes are based on nested MDS code and represent a generalized form of the minimum bandwidth regenerating (MBR) codes in traditional DSSs.

On a highway, the frequency of occurrence of irrelevant features, such as trees, varies a lot in different scenes. A limitation of the deep conventional neural networks used in end-to-end self-driving systems is that if the incoming images contain too much information, it makes it difficult for the network to extract only the subset of features required for decision making. Consequently, while existing end-to-end approaches may perform well in training scenes, they may not work correctly in other scenes. In this study, we developed a novel training method for an auto-encoder that equips it to ignore irrelevant features in input images while simultaneously retaining relevant features. Compared with feature extraction methods in existing end-to-end approaches, the proposed method reduces the labeling costs by only requiring image-level tags. The method was validated by training a convolutional neural network model to process the output of the encoder and produce a steering angle to control the vehicle. The entire end-to-end self-driving approach can ignore the influence of irrelevant features even though there are no such features when training the convolutional neural network.