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This paper presents a novel anomaly detection methodology termed Statistical Aggregated Anomaly Detection (SAAD). The SAAD approach integrates advanced statistical techniques with machine learning, and its efficacy is demonstrated through…

AI/ML-based intrusion detection systems (IDSs) and misbehavior detection systems (MDSs) have shown great potential in identifying anomalies in the network traffic of networked autonomous systems. Despite the vast research efforts, practical…

End-to-end autonomous driving solutions, which process multi-modal sensory data to directly generate refined control commands, have become a dominant paradigm in autonomous driving research. However, these approaches predominantly depend on…

The autonomous car technology promises to replace human drivers with safer driving systems. But although autonomous cars can become safer than human drivers this is a long process that is going to be refined over time. Before these vehicles…

This paper presents crash rate benchmarks for evaluating US-based Automated Driving Systems (ADS) for multiple urban areas. The purpose of this study was to extend prior benchmarks focused only on surface streets to additionally capture…

Navigation and positioning systems dependent on both the operating environment and the behaviour of the host vehicle or user. The environment determines the type and quality of radio signals available for positioning and the behaviour can…

The recent surge in interest in autonomous driving stems from its rapidly developing capacity to enhance safety, efficiency, and convenience. A pivotal aspect of autonomous driving technology is its perceptual systems, where core algorithms…

Autonomous vehicles rely on camera, LiDAR, and radar sensors to navigate the environment. Adverse weather conditions like snow, rain, and fog are known to be problematic for both camera and LiDAR-based perception systems. Currently, it is…

A major bottleneck in off-road autonomous driving research lies in the scarcity of large-scale, high-quality datasets and benchmarks. To bridge this gap, we present ORAD-3D, which, to the best of our knowledge, is the largest dataset…

For an autonomous vehicle, the ability to sense its surroundings and to build an overall representation of the environment by fusing different sensor data streams is fundamental. To this end, the poses of all sensors need to be accurately…

Autonomous driving systems continue to face safety-critical failures, often triggered by rare and unpredictable corner cases that evade conventional testing. We present the Autonomous Driving Digital Twin (ADDT) framework, a high-fidelity…

This paper presents a computationally efficient method for vehicle speed estimation from traffic camera footage. Building upon previous work that utilizes 3D bounding boxes derived from 2D detections and vanishing point geometry, we…

For intelligent vehicles, sensing the 3D environment is the first but crucial step. In this paper, we build a real-time advanced driver assistance system based on a low-power mobile platform. The system is a real-time multi-scheme…

In this paper, we compare three different model-based risk measures by evaluating their stengths and weaknesses qualitatively and testing them quantitatively on a set of real longitudinal and intersection scenarios. We start with the…

Detection of surrounding objects and their motion prediction are critical components of a self-driving system. Recently proposed models that jointly address these tasks rely on a number of sensors to achieve state-of-the-art performance.…

Automated Driving System (ADS) has attracted increasing attention from both industrial and academic communities due to its potential for increasing the safety, mobility and efficiency of existing transportation systems. The state-of-the-art…

The monitoring of the route and track environment plays an important role in automated driving. For example, it can be used as an assistance system for route monitoring in automation level Grade of Automation (GoA) 2, where the train driver…

The progress of Anomaly Detection (AD) in safety-critical domains, such as transportation, is severely constrained by the lack of large-scale, real-world benchmarks. To address this, we introduce EngineAD, a novel, multivariate dataset…

Autonomous and Robotics Systems (ARSs) are widespread, complex, and increasingly coming into contact with the public. Many of these systems are safety-critical, and it is vital to detect software errors to protect against harm. We propose a…

Vehicles with driving automation are increasingly being developed for deployment across the world. However, the onboard sensing and perception capabilities of such automated or autonomous vehicles (AV) may not be sufficient to ensure safety…