Exploring the effects of perception errors and anticipation strategies on traffic accidents - A simulation study

It is remarkable that drivers (on average) can safely navigate through dense traffic at high speeds—conditions in which the time headways between vehicles are in the same order of magnitude as human reaction times. One explanation for this is the ability of drivers to anticipate on the traffic conditions in their surroundings. In this paper,...

A risk-based driver behaviour model

Current driver behaviour models (DBMs) are primarily designed for the general driver population under specific scenarios, such as car following or lane changing. Hence DBMs capturing individual behaviour under various scenarios are lacking. This paper presents a novel method to quantify individual perceived driving risk in the longitudinal...

Sensing and Machine Learning for Automotive Perception: A Review

Automotive perception involves understanding the external driving environment and the internal state of the vehicle cabin and occupants using sensor data. It is critical to achieving high levels of safety and autonomy in driving. This article provides an overview of different sensor modalities, such as cameras, radars, and light detection and...

Reliability improvement of the dredging perception system: A sensor fault-tolerant strategy

In the dredging industry, the automation and accuracy of the Dredging Perception System (DPS) are vital for operational efficiency and environmental safety. Current DPS implementations face challenges with sensor fault tolerance, leading to system unreliability and increased false alarm rates that can disrupt dredging operations. We propose a...