In the past decade, autonomous vehicle systems (AVS) have advanced at an exponential
rate, particularly due to improvements in artificial intelligence, which have had a significant …

Inscrutable AI systems are difficult to trust, especially if they operate in safety-critical settings
like autonomous driving. Therefore, there is a need to build transparent and queryable …

The recent proliferation of computing technologies (eg, sensors, computer vision, machine
learning, and hardware acceleration) and the broad deployment of communication …

Large-scale deployment of autonomous vehicles has been continually delayed due to safety
concerns. On the one hand, comprehensive scene understanding is indispensable, a lack of …

Neural networks in autonomous vehicles suffer from overfitting, poor generalizability, and
untrained edge cases due to limited data availability. Researchers often synthesize …

Autonomous driving is of great interest to industry and academia alike. The use of machine
learning approaches for autonomous driving has long been studied, but mostly in the …

Predicting traffic risk incidents in first-person helps to ensure a safety reaction can occur
before the incident happens for a wide range of driving scenarios and conditions. One …

Human drivers have excellent perception and reaction abilities in complex environments
such as dangerous highways, busy intersections, and harsh weather conditions. To achieve …

As a key technology of intelligent transportation system, the intelligent vehicle is the carrier
of comprehensive integration of many technologies. Although vision-based autonomous …

Autonomous driving models should ideally be evaluated by deploying them on a fleet of
physical vehicles in the real world. Unfortunately, this approach is not practical for the vast …