Intelligent Anomaly Detection for Lane Rendering Using Transformer with Self-Supervised Pre-Training and Customized Fine-Tuning

The burgeoning navigation services using digital maps provide great convenience to drivers. However, there are sometimes anomalies in the lane rendering map images, which might mislead human drivers and result in unsafe driving. To accurately and effectively detect the anomalies, this paper transforms lane rendering image anomaly detection into...

Data-driven Semi-supervised Machine Learning with Surrogate Safety Measures for Abnormal Driving Behavior Detection

Detecting abnormal driving behavior is critical for road traffic safety and the evaluation of drivers' behavior. With the advancement of machine learning (ML) algorithms and the accumulation of naturalistic driving data, many ML models have been adopted for abnormal driving behavior detection. Most existing ML-based detectors rely on (fully)...

Social-aware Planning and Control for Automated Vehicles Based on Driving Risk Field and Model Predictive Contouring Control: Driving through Roundabouts as a Case Study

The gradual deployment of automated vehicles (AVs) results in mixed traffic where AVs will interact with human-driven vehicles (HDVs). Thus, social-aware motion planning and control while considering interactions with HDVs on the road is critical for AVs’ deployment and safe driving under various maneuvers. Previous research mostly focuses on...

A Hybrid Spatial-temporal Sequence-to-one Neural Network Model for Lane Detection

Reliable and accurate lane detection is of vital importance for the safe performance of Lane Keeping Assistance and Lane Departure Warning systems. However, under certain challenging peculiar circumstances (e.g., marking degradation, serious vehicle occlusion), it is quite difficult to get satisfactory performance in accurately detecting the...

Road Infrastructure Requirements for Improved Performance of Lane Assistance Systems

There is a pressing need for road authorities to take a proactive role in the deployment of automated vehicles on the existing road network. This requires a comprehensive understanding of the road infrastructure requirements that would lead to safe operation of automated vehicles. In this context, a field test with Lane Departure Warning and...

Travel Times in Quasi-Dynamic Traffic Assignment

By extending static traffic assignment with explicit capacity constraints, quasidynamic traffic assignment yields more realistic results while avoiding many disadvantages of dynamic assignment. We analyse the computation of travel times in quasi-dynamic assignment models.<br/><br/>We formulate and check requirements for the correctness of...

Meaningful Human Control over Automated Driving Systems