The grid-centric perception is a crucial field for mobile robot perception and navigation.
Nonetheless, the grid-centric perception is less prevalent than object-centric perception as …

Machine Learning (ML) has replaced handcrafted methods for perception and prediction in
autonomous vehicles. Yet for the equally important planning task, the adoption of ML-based …

As autonomous driving systems being deployed to millions of vehicles, there is a pressing
need of improving the system's scalability, safety and reducing the engineering cost. A …

In recent years, imitation-based driving planners have reported considerable success.
However, due to the absence of a standardized benchmark, the effectiveness of various …

Automated driving has the potential to revolutionize personal, public, and freight mobility.
Beside accurately perceiving the environment, automated vehicles must plan a safe …

Vehicle motion planning is an essential component of autonomous driving technology.
Current rule-based vehicle motion planning methods perform satisfactorily in common …

Large real-world driving datasets have sparked significant research into various aspects of
data-driven motion planners for autonomous driving. These include data augmentation …

This paper aims to provide a quick review of the methods including the technologies in detail
that are currently reported in industry and academia. Specifically, this paper reviews the end …

Autonomous driving demands proficient trajectory planning to ensure safety and comfort.
This letter introduces Policy-Embedded Planner (PEP), a novel framework that enhances …

The utilization of vector-based deep learning techniques has great prospects in the realm of
autonomous driving, particularly in the domains of prediction and planning tasks. However …