Supervised open-loop training has been widely adopted for training traffic simulation models; however, it fails to capture the inherently dynamic, multi-agent interactions …
Trajectory prediction models in autonomous driving are vulnerable to perturbations from non-causal agents whose actions should not affect the ego-agent’s behavior. Such …
In this paper, we introduce Context-Aware Priority Sampling (CAPS), a novel method designed to enhance data efficiency in learning-based autonomous driving systems. CAPS addresses …
Motion planning is a fundamental problem in autonomous driving and perhaps the most challenging to comprehensively evaluate because of the associated risks and expenses of …
Interaction quality improvement in a social robotic platform can be achieved through intention detection/prediction of the user. In this research, we tried to study the effect of …
Technology is inevitable, and its role for clinical therapists and specialists cannot be ignored. The promising movement towards computer-based interventions, specifically the use …