Large-scale deployment of fully autonomous vehicles requires a very high degree of
robustness to unstructured traffic, weather conditions, and should prevent unsafe …

Machine learning (ML) datasets, often perceived as" neutral," inherently encapsulate
abstract and disputed social constructs. Dataset curators frequently employ value-laden …

A self-driving car must be able to reliably handle adverse weather conditions (eg, snowy) to
operate safely. In this paper, we investigate the idea of turning sensor inputs (ie, images) …

Public datasets such as KITTI nuScenes and Waymo have played a key role in the research
and development of autonomous vehicles and advanced driver assistance systems …

The uncertainty quantification of prediction models (eg, neural networks) is crucial for their
adoption in many robotics applications. This is arguably as important as making accurate …

We present TimeNeRF, a generalizable neural rendering approach for rendering novel
views at arbitrary viewpoints and at arbitrary times, even with few input views. For real-world …

Predicting agents' trajectories in complex environments is critical for achieving safe
autonomous robot navigation. Empirically, agents' decisions and preferences are …

The evolution of autonomous vehicles is advancing rapidly, promising a radical shift in our
future mobility. The cornerstone of building a reliable autonomous vehicle hinges on …

Abstract Autonomous Ground Vehicles (AGVs) have made their way into various industries,
including transportation, delivery services, healthcare, and logistics handling. In order to …

Autonomous driving is a dynamic field of research that has garnered significant attention
and effort from numerous researchers. Its progress has been greatly propelled by the …