相关论文: INTERNEURON: A Middleware with Multi-Network Commu…

Automotive FMCW radars remain reliable in rain and glare, yet their sparse, noisy point clouds constrain 3-D object detection. We therefore release CoVeRaP, a 21 k-frame cooperative dataset that time-aligns radar, camera, and GPS streams…

Reliable and secure communication is essential for mission-critical aerospace and defence operations involving autonomous platforms such as Unmanned Aerial Vehicles (UAVs), satellites, and ground control systems. In contested or dynamic…

High-speed cruising scenarios with mixed traffic greatly challenge the road safety of autonomous vehicles (AVs). Unlike existing works that only look at fundamental modules in isolation, this work enhances AV safety in mixed-traffic…

The growing interest in autonomous driving calls for realistic simulation platforms capable of accurately simulating cooperative perception process in realistic traffic scenarios. Existing studies for cooperative perception often have not…

We address the security of a network of Connected and Automated Vehicles (CAVs) cooperating to navigate through a conflict area. Adversarial attacks such as Sybil attacks can cause safety violations resulting in collisions and traffic jams.…

Accurately predicting the trajectory of surrounding vehicles is a critical challenge for autonomous vehicles. In complex traffic scenarios, there are two significant issues with the current autonomous driving system: the cognitive…

Intersection crossing represents a bottleneck for transportation systems and Connected Autonomous Vehicles (CAVs) may be the groundbreaking solution to the problem. This work proposes a novel framework, i.e, AVOID-PERIOD, where an…

Security is of primary importance to vehicles. The viability of performing remote intrusions onto the in-vehicle network has been manifested. In regard to unmanned autonomous cars, limited work has been done to detect intrusions for them…

This survey analyzes intermediate fusion methods in collaborative perception for autonomous driving, categorized by real-world challenges. We examine various methods, detailing their features and the evaluation metrics they employ. The…

The safety of single-vehicle autonomous driving technology is limited due to the limits of perception capability of on-board sensors. In contrast, vehicle-road collaboration technology can overcome those limits and improve the traffic…

Autonomous vehicles often make complex decisions via machine learning-based predictive models applied to collected sensor data. While this combination of methods provides a foundation for real-time actions, self-driving behavior primarily…

Expected to provide higher transportation efficiency and security, autonomous driving has attracted substantial attentions from both industry and academia. Meanwhile, the emergence of edge intelligence has further introduced significant…

In the modern era of automation and robotics, autonomous vehicles are currently the focus of academic and industrial research. With the ever increasing number of unmanned aerial vehicles getting involved in activities in the civilian and…

Connected vehicle (CV) technology is among the most heavily researched areas in both the academia and industry. The vehicle to vehicle (V2V), vehicle to infrastructure (V2I) and vehicle to pedestrian (V2P) communication capabilities enable…

Navigation is a very crucial aspect of autonomous vehicle ecosystem which heavily relies on collecting and processing large amounts of data in various states and taking a confident and safe decision to define the next vehicle maneuver. In…

Autonomous vehicles (AVs) are poised to redefine transportation by enhancing road safety, minimizing human error, and optimizing traffic efficiency. The success of AVs depends on their ability to interpret complex, dynamic environments…

Today, with the development of intercity and metropolitan roadways and with various cars moving in various directions, there is a greater need than ever for a network to coordinate commutes. Nowadays, people spend a lot of time in their…

With advances in sensing, computing, and communication technologies, Connected and Automated Vehicles (CAVs) are becoming feasible. The advent of CAVs presents new opportunities to improve the energy efficiency of individual vehicles.…

Cooperative decision-making of Connected Autonomous Vehicles (CAVs) presents a longstanding challenge due to its inherent nonlinearity, non-convexity, and discrete characteristics, compounded by the diverse road topologies encountered in…

In mixed-traffic environments, autonomous vehicles (AVs) must interact with heterogeneous human-driven vehicles (HVs) whose intentions and driving styles vary across individuals and scenarios. Such variability introduces uncertainty into…