A semantic understanding of road traffic can help people understand road traffic flow
situations and emergencies more accurately and provide a more accurate basis for anomaly …

Automated and autonomous vehicles are often required to operate in complex road
environments with potential hazards that may lead to hazardous events causing injury or …

User trust has been identified as a critical issue that is pivotal to the success of autonomous
vehicle (AV) operations where artificial intelligence (AI) is widely adopted. For such …

With the increasing deployment of roadside sensors, vehicle trajectories can be collected for
driving behavior analysis and vehicle-highway automation systems. However, due to …

For autonomous driving, one of the major challenges is to predict pedestrian crossing
intention in ego-view. Pedestrian intention depends not only on their intrinsic goals but also …

In complex and dynamic urban traffic scenarios, the accurate prediction of trajectories of
surrounding traffic participants (vehicles, pedestrians, etc) with interactive behaviours plays …

There is considerable evidence that evaluating the subjective risk level of driving decisions
can improve the safety of Autonomous Driving Systems (ADS) in both typical and complex …

In autonomous vehicles (AVs), early warning systems rely on collision prediction to ensure
occupant safety. However, state-of-the-art methods using deep convolutional networks …

Temporal modelling is the key for efficient video action recognition. While understanding
temporal information can improve recognition accuracy for dynamic actions, removing …

Egocentric videos can bring a lot of information about how humans perceive the world and
interact with the environment, which can be beneficial for the analysis of human behaviour …