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 …

Current end-to-end autonomous driving methods either run a controller based on a planned
trajectory or perform control prediction directly, which have spanned two separately studied …

The autonomous driving community has witnessed a rapid growth in approaches that
embrace an end-to-end algorithm framework, utilizing raw sensor input to generate vehicle …

Driving safety has been a concern since the first cars appeared on the streets. Driver
inattention has been singled out as a major cause of accidents early on. This is hardly …

In recent years, great efforts have been devoted to deep imitation learning for autonomous
driving control, where raw sensory inputs are directly mapped to control actions. However …

Previous multi-task dense prediction methods based on the Mixture of Experts (MoE) have
received great performance but they neglect the importance of explicitly modeling the global …

Multi-task learning has emerged as a powerful paradigm to solve a range of tasks
simultaneously with good efficiency in both computation resources and inference time …

Focusing on the task of point-to-point navigation for an autonomous driving vehicle, we
propose a novel deep learning model trained with end-to-end and multi-task learning …

This paper explores the emerging knowledge-driven autonomous driving technologies. Our
investigation highlights the limitations of current autonomous driving systems, in particular …

Multi-task learning faces two challenging issues:(1) the high cost of annotating labels for all
tasks and (2) balancing the training progress of various tasks with different natures. To …