Ascertaining the precise detection of traffic objects, drivable areas, and lane lines is the
foremost obligation for an automatic driving system. The efficient execution of these tasks is …

Visual perception is a critical task for autonomous driving. Understanding the driving
environment in real time can assist a vehicle in driving safely. In this study, we proposed a …

Real-time processing is crucial in autonomous driving systems due to the imperative of
instantaneous decision-making and rapid response. In real-world scenarios, autonomous …

With the rapid development of intelligent driving vehicles, multi-task visual perception based
on deep learning emerges as a key technological pathway toward safe vehicle navigation in …

Autonomous driving is a popular research area within the computer vision research
community. Since autonomous vehicles are highly safety-critical, ensuring robustness is …

In autonomous driving, perception plays a critical role as it serves as a fundamental
requirement for both planning and control. Currently, most perception tasks are processed …

Perception is an essential task for self-driving cars, but most perception tasks are usually
handled independently. We propose a unified neural network named DLT-Net to detect …

A vision-based autonomous driving perception system necessitates the accomplishment of a
suite of tasks, including vehicle detection, drivable area segmentation, and lane line …

In autonomous driving, environmental perception technology often encounters challenges
such as false positives, missed detections, and low accuracy, particularly in detecting small …

The visual perception model is critical to autonomous driving systems. It provides the
information necessary for self-driving cars to make decisions in traffic scenes. We propose a …