Environmental perception with the multi-modal fusion is crucial in autonomous driving to
increase accuracy, completeness, and robustness. This paper focuses on utilizing millimeter …

The accurate detection and classification of moving objects is a critical aspect of advanced
driver assistance systems. We believe that by including the object classification from multiple …

With the potential to increase road safety and provide economic benefits, intelligent vehicles
have elicited a significant amount of interest from both academics and industry. A robust and …

In the context of autonomous driving environment perception, multi-modal fusion plays a
pivotal role in enhancing robustness, completeness, and accuracy, thereby extending the …

Moving-objects detection is a critical ability for an autonomous vehicle. Facing the high
detection requirements and the slow target-extraction problem of a common camera, this …

Purpose-The purpose of this paper is to accurately capture the risks which are caused by
each road user in time. Design/methodology/approach-The authors proposed a novel risk …

多模态融合感知是自动驾驶的研究热点之一, 然而在复杂交通环境下由于天气,
光照等外部因素干扰, 目标识别可能出现错误, 融合时会不可避免地出现分类冲突问题. 为此 …

For intelligent vehicles, multi-sensor fusion is of great importance to perceive traffic
environment with high accuracy and robustness. In this paper, we propose two effective …

Leg tracking is an established field in mobile robotics and machine vision in general. These
algorithms, however, only distinguish the scene between leg and nonleg detections. In …

Detection and classification of moving objects is essential for autonomous driving. To tackle
this problem, this paper proposes an object classification method at detection level using a …