The virtual-to-real paradigm, ie, training models on virtual data and then applying them to
solve real-world problems, has attracted more and more attention from various domains by …

End-to-End driving is a promising paradigm as it circumvents the drawbacks associated with
modular systems, such as their overwhelming complexity and propensity for error …

Develo** autonomous driving technologies necessitates addressing safety and cost
concerns. Both academic research and commercial applications of autonomous driving …

Synchronizing decisions across multiple agents in realistic settings is problematic since it
requires agents to wait for other agents to terminate and communicate about termination …

While engaging with the unfolding revolution in autonomous driving, a challenge presents
itself, how can we effectively raise awareness within society about this transformative trend …

In today's fast-paced world, everything around us is automatized and customized as per our
needs. Many researchers forecast that by 2025 we'll be seeing approximately 8 million …

The autonomous driving is increasingly mounting, promoting, and promising the future of
fully autonomous and, correspondingly presenting new challenges in the field of safety …

Many multi-robot planning problems are burdened by the curse of dimensionality, which
compounds the difficulty of applying solutions to large-scale problem instances. The use of …

Although the interest in autonomous vehicles and advanced driver assistance systems
(ADAS) is significantly increasing, they won't be viable until their system outperforms human …

One significant challenge in improving autonomous driving algorithms is the lack of diverse
real-world data. Moreover, transferring models from simulation to reality faces the reality gap …