Interactive traffic simulation is crucial to autonomous driving systems by enabling testing for
planners in a more scalable and safe way compared to real-world road testing. Existing …

Driving is a social activity which involves endless interactions with other agents on the road.
Failing to locate these agents and predict their possible future actions may result in serious …

Scenario-based testing for automated driving systems (ADS) must be able to simulate traffic
scenarios that rely on interactions with other vehicles. Although many languages for high …

Motivated by the need for simulation tools for testing, verification and validation of
autonomous driving systems that operate in traffic consisting of both autonomous and …

Smart traffic control and management become an emerging application for Deep
Reinforcement Learning (DRL) to solve traffic congestion problems in urban networks …

Digital Twin is an emerging technology that replicates real-world entities into a digital space.
It has attracted increasing attention in the transportation field and many researchers are …

We present an agent-based approach to animate microscopic mixed traffic involving cars
and motorcycles in complex scenarios, including signalized and nonsignalized road …

Generating multi-vehicle interaction scenarios can benefit motion planning and decision
making of autonomous vehicles when on-road data is insufficient. This paper presents an …

Examining driving behaviour is crucial for traffic operations because of its influence on driver
safety and the potential for increased risk of accidents, injuries, and fatalities. Approximately …

To facilitate automation of urban driving, we present an efficient, lightweight, open-source,
first-order simulator with associated graphical display and algorithmic supervisors. FLUIDS …