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The following paper introduces a novel integrated sensing and communication (ISAC) scenario termed hybrid radar fusion. In this setting, the dual-functional radar and communications (DFRC) base station (BS) acts as a mono-static radar in…

When driving,it is vital to maintain the right following distance between the vehicles to avoid rear-end collisions. The minimum safe distance depends on many factors, however, in this study the safe distance between the human-driven…

Before reaching full autonomy, vehicles will gradually be equipped with more and more advanced driver assistance systems (ADAS), effectively rendering them semi-autonomous. However, current ADAS technologies seem unable to handle complex…

This paper introduces a real-time method for reverse engineering a vehicle's CAN bus without prior knowledge of the vehicle or its CAN system. By comparing inertial measurement and CAN data during significant vehicle events, the method…

Advanced Driver Assistance Systems (ADAS) and Advanced Driving Systems (ADS) are key to improving road safety, yet most existing implementations focus primarily on the vehicle ahead, neglecting the behavior of following vehicles. This…

This paper presents Lidar-based Simultaneous Localization and Mapping (SLAM) for autonomous driving vehicles. Fusing data from landmark sensors and a strap-down Inertial Measurement Unit (IMU) in an adaptive Kalman filter (KF) plus the…

One of the main challenges in developing autonomous transport systems based on connected and automated vehicles is the comprehension and understanding of the environment around each vehicle. In many situations, the understanding is limited…

This work aims to train Deep Learning models to perform Automatic Target Recognition (ATR) on Synthetic Aperture Radar (SAR) images. To circumvent the lack of real labelled measurements, we resort to synthetic data produced by SAR…

This paper presents a scenario generation framework that creates diverse, parametrized, and safety-critical driving situations to validate the safety features of autonomous vehicles in simulation [15]. By modeling factors such as road…

This paper presents a digital-twin platform for active safety analysis in mixed traffic environments. The platform is built using a multi-modal data-enabled traffic environment constructed from drone-based aerial LiDAR, OpenStreetMap, and…

Radars and cameras are mature, cost-effective, and robust sensors and have been widely used in the perception stack of mass-produced autonomous driving systems. Due to their complementary properties, outputs from radar detection (radar…

Driven by deep learning techniques, perception technology in autonomous driving has developed rapidly in recent years, enabling vehicles to accurately detect and interpret surrounding environment for safe and efficient navigation. To…

This paper presents a coupled, neural network-aided longitudinal cruise and lateral path-tracking controller for an autonomous vehicle with model uncertainties and experiencing unknown external disturbances. Using a feedback error learning…

Radar has stronger adaptability in adverse scenarios for autonomous driving environmental perception compared to widely adopted cameras and LiDARs. Compared with commonly used 3D radars, the latest 4D radars have precise vertical resolution…

The growing demand for robust scene understanding in mobile robotics and autonomous driving has highlighted the importance of integrating multiple sensing modalities. By combining data from diverse sensors like cameras and LIDARs, fusion…

This work deals with integration and transmission of safety information for smart railway vehicles and control and design of cooperative emergency brake systems for high speed trains. Due to increased speed of high-speed train, safety of…

While automotive radar sensors are widely adopted and have been used for automatic cruise control and collision avoidance tasks, their application outside of vehicles is still limited. As they have the ability to resolve multiple targets in…

Along with the improvement of radar technologies, Automatic Target Recognition (ATR) using Synthetic Aperture Radar (SAR) and Inverse SAR (ISAR) has come to be an active research area. SAR/ISAR are radar techniques to generate a…

Autonomous driving has now made great strides thanks to artificial intelligence, and numerous advanced methods have been proposed for vehicle end target detection, including single sensor or multi sensor detection methods. However, the…

As self-driving systems become better, simulating scenarios where the autonomy stack may fail becomes more important. Traditionally, those scenarios are generated for a few scenes with respect to the planning module that takes ground-truth…