Autonomous vehicles (AVs) will operate alongside human-driven vehicles for an extended transition period, during which aggressive human driving may become a major source of risk. https://www.mdpi.com/2076-3417/16/16/7908...
Read MoreAutonomous driving systems convert environmental perception into vehicle motion. This process relies on a layered architecture composed of perception, planning and control. Among these layers, the control is considered the...
Read MorePrecise trajectory tracking holds significant importance for autonomous vehicle navigation. Conventional lateral control strategies are oriented towards eliminating yaw angle deviation....
Read MoreAutonomous Vehicles (AV), AI systems that perceive, reason, and act in the physical world, have advanced more slowly despite substantial public and private investment. https://www.mdpi.com/2673-2688/7/8/275...
Read MoreAutonomous driving technology is regarded as an effective approach to improving traffic safety and reducing accidents. However, its performance in complex urban scenarios, particularly at intersections, still requires...
Read MoreJune 10, 2026 Ensuring safety in autonomous vehicles (AVs) requires predictive control methods that can handle dynamic constraints, uncertain interactions, and real-time decision making. https://www.mdpi.com/2673-4052/7/3/88...
Read MoreThe emergence of connected autonomous vehicles (CAVs) represents a transformative shift in transportation systems, promising enhanced road safety, improved traffic efficiency, and reduced environmental impact....
Read MoreThe last few years have seen significant developments in automated vehicle (AV) technologies and their commercialization. https://www.nature.com/articles/s44333-026-00095-3
Read MoreAutonomous vehicles (AVs) are diffusing in cities worldwide, propelled by expectations for enhanced safety and sustainability. https://www.nature.com/articles/s44333-026-00096-2
Read MoreAutonomous vehicles are essentially data centers on wheels. Between self-driving algorithms and high-definition mapping, the demand for instant processing at the network edge is exploding....
Read MoreAutonomous driving, despite significant progress, is still not widely applied in open, unconstrained environments, primarily owing to deficiencies in hazard perception, few-shot generalization, corner case generalization, and...
Read MoreAutonomous vehicles (AVs), or self-driving cars, are vehicles capable of sensing their environment and operating without human involvement, using technologies such as radar, LiDAR, and computer vision....
Read MoreAutonomous driving systems are often discussed in terms of AI capabilities or high-level ethics. However, for the software architects and engineers building these systems, the reality is a battle against latency, bandwidth, and...
Read MoreAutonomous vehicles (AVs) tend to disrupt the atmosphere and pedestrian experience in urban shared spaces, undermining the focus of these spaces on people and placemaking. https://arxiv.org/html/2603.27933v1#S6
Read MoreBy integrating spatiotemporal radar-based precipitation nowcasting, deep neural network-based sensor degradation modelling, and adaptive probabilistic sensor fusion, the system dynamically adjusts LiDAR, RADAR, and camera inputs...
Read MoreReal-time object detection in adverse weather and low-light conditions is crucial for applications such as autonomous driving and intelligent surveillance. https://www.nature.com/articles/s41598-026-40319-7
Read MoreLocal path tracking is a critical challenge for autonomous vehicles, requiring precise trajectory following under nonlinear dynamics, strict constraints, and real-time execution....
Read MoreHuman drivers naturally balance the risks of different concerns while driving, including traffic rule violations, minor accidents, and fatalities. https://arxiv.org/html/2409.09769v4
Read MoreConnected and Autonomous Vehicles (CAVs) operate in dynamic and heterogeneous environments where sensing, computation, communication, and control must continuously adapt to changing road, weather, and traffic conditions....
Read MoreAs the research on self-driving systems and autonomous vehicles shift from rule-based autonomy to AIdriven autonomy, machine-learning (ML) inference is used increasingly to support perception, prediction and planning with strict...
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