Related papers: CMP: Cooperative Motion Prediction with Multi-Agen…
Cooperative driving, enabled by Vehicle-to-Everything (V2X) communication, is expected to significantly contribute to the transportation system's safety and efficiency. Cooperative Adaptive Cruise Control (CACC), a major cooperative driving…
Cooperative perception, offering a wider field of view than standalone perception, is becoming increasingly crucial in autonomous driving. This perception is enabled through vehicle-to-vehicle (V2V) communication, allowing connected…
Cooperative perception of connected vehicles comes to the rescue when the field of view restricts stand-alone intelligence. While raw-level cooperative perception preserves most information to guarantee accuracy, it is demanding in…
Environmental perception in Automated Valet Parking (AVP) has been a challenging task due to severe occlusions in parking garages. Although Collaborative Perception (CP) can be applied to broaden the field of view of connected vehicles, the…
In cooperative ITS, security and privacy protection are essential. Cooperative Awareness Message (CAM) is a basic V2V message standard, and misbehavior detection is critical for protection against attacking CAMs from the inside system, in…
Cooperative perception extends the perception capabilities of autonomous vehicles by enabling multi-agent information sharing via Vehicle-to-Everything (V2X) communication. Unlike traditional onboard sensors, V2X acts as a dynamic…
Autonomous motion capture (mocap) systems for outdoor scenarios involving flying or mobile cameras rely on i) a robotic front-end to track and follow a human subject in real-time while he/she performs physical activities, and ii) an…
Anomaly detection is a critical requirement for ensuring safety in autonomous driving. In this work, we leverage Cooperative Perception to share information across nearby vehicles, enabling more accurate identification and consensus of…
Urban intersections, dense with pedestrian and vehicular traffic and compounded by GPS signal obstructions from high-rise buildings, are among the most challenging areas in urban traffic systems. Traditional single-vehicle intelligence…
Vehicle-to-Everything (V2X) cooperation is reshaping traffic safety from an ego-centric sensing problem into one of collective intelligence. This survey structures recent progress within a unified Sensor-Perception-Decision (SPD) framework…
Temporal perception, defined as the capability to detect and track objects across temporal sequences, serves as a fundamental component in autonomous driving systems. While single-vehicle perception systems encounter limitations, stemming…
Connected Autonomous Vehicles have great potential to improve automobile safety and traffic flow, especially in cooperative applications where perception data is shared between vehicles. However, this cooperation must be secured from…
Vehicle-to-everything (V2X) technologies offer a promising paradigm to mitigate the limitations of constrained observability in single-vehicle systems. Prior work primarily focuses on single-frame cooperative perception, which fuses agents'…
Unmanned Aerial Vehicle (UAV) swarm systems necessitate efficient collaborative perception mechanisms for diverse operational scenarios. Current Bird's Eye View (BEV)-based approaches exhibit two main limitations: bounding-box…
Motion prediction for intelligent vehicles typically focuses on estimating the most probable future evolutions of a traffic scenario. Estimating the gap acceptance, i.e., whether a vehicle merges or crosses before another vehicle with the…
Human motion prediction is important for mobile service robots and intelligent vehicles to operate safely and smoothly around people. The more accurate predictions are, particularly over extended periods of time, the better a system can,…
Current autonomous driving vehicles rely mainly on their individual sensors to understand surrounding scenes and plan for future trajectories, which can be unreliable when the sensors are malfunctioning or occluded. To address this problem,…
Multi-object tracking (MOT) is critical in numerous real-world applications, including surveillance, autonomous driving, and robotics. Accurately predicting object motion is fundamental to MOT, but current methods struggle with the…
Situational awareness as a necessity in the connected and autonomous vehicles (CAV) domain is the subject of a significant number of researches in recent years. The driver's safety is directly dependent on the robustness, reliability, and…
Vehicle-to-Infrastructure (V2I) collaborative perception leverages data collected by infrastructure's sensors to enhance vehicle perceptual capabilities. LiDAR, as a commonly used sensor in cooperative perception, is widely equipped in…