Recently, mobility has gathered tremendous interest as the users' desire for consecutive
connections and better quality of service has increased. An accurate prediction of user …

Real-time risk assessment of autonomous driving at tactical and operational levels is
extremely challenging since both contextual and circumferential factors should concurrently …

Machine learning approaches often lack safety guarantees, which are often a key
requirement in real-world tasks. This paper addresses the lack of safety guarantees by …

Recently, inferring lane change intention has received considerable attention. Due to the
high nonlinearity and complexity of traffic contexts, traditional methods cannot satisfy the …

Motion planning is one of the most significant technologies for autonomous driving. To make
motion planning models able to learn from the environment and to deal with emergency …

We study the design of learning architectures for behavioural planning in a dense traffic
setting. Such architectures should deal with a varying number of nearby vehicles, be …

This paper surveys the state-of-the-art contributions supporting the validation of virtual
testing toolchains for Automated Driving System (ADS) verification. The work builds upon the …

The significant contribution of human errors, accounting for approximately 94%(with a
margin of±2.2%), to road crashes leading to casualties, vehicle damages, and safety …

Anticipating possible behaviors of traffic participants is an essential capability of
autonomous vehicles. Many behavior detection and maneuver recognition methods only …

Intelligent transportation systems, or ITS for short, includes a variety of services and
applications such as road traffic management, traveler information systems, public transit …