Autonomous vehicles (AV) are game-changing innovations that promise a safer, more
convenient, and environmentally friendly mode of transportation than traditional vehicles …

Applying and adapting deep reinforcement learning (DRL) to tackle transportation problems
is an emerging interdisciplinary field. While rapidly growing, a comprehensive and synthetic …

With the development of deep representation learning, the domain of reinforcement learning
(RL) has become a powerful learning framework now capable of learning complex policies …

You are holding in your hands… oh, come on, who holds books like this in their hands
anymore? Anyway, you are reading this, and it means that I have managed to release one of …

Object detection and data association are critical components in multi-object tracking (MOT)
systems. Despite the fact that the two components are dependent on each other, prior works …

3D multi-object tracking (MOT) is an essential component for many applications such as
autonomous driving and assistive robotics. Recent work on 3D MOT focuses on develo** …

In recent years, deep learning models have demonstrated remarkable success in various
domains, such as computer vision, natural language processing, and speech recognition …

Abstract 3D Multi-object tracking (MOT) is crucial to autonomous systems. Recent work uses
a standard tracking-by-detection pipeline, where feature extraction is first performed …

Monocular 3D scene understanding tasks, such as object size estimation, heading angle
estimation and 3D localization, is challenging. Successful modern-day methods for 3D …

Autonomous driving (AD) agents generate driving policies based on online perception
results, which are obtained at multiple levels of abstraction, eg, behavior planning, motion …