期刊
EXPERT SYSTEMS WITH APPLICATIONS
卷 238, 期 -, 页码 -出版社
PERGAMON-ELSEVIER SCIENCE LTD
DOI: 10.1016/j.eswa.2023.121983
关键词
Deep learning; Autonomous vehicle; Pedestrian; Vehicles; Behaviour prediction
Autonomous vehicles have the potential to solve traffic problems, but there is still room for improvement. This paper presents a review of state-of-the-art algorithms proposed for AV behavior prediction systems.
Autonomous vehicles (AV)s have become a trending topic nowadays since they have the potential to solve traffic problems, such as accidents and congestion. Although AV systems have greatly evolved, it still have their limitations. For example, Google reported that their AVs have been involved in several collisions and near misses. While most of these collisions and near misses were caused by third parties, the AVs should be able to predict and avoid them. Events like this show that there is still room for improvement in the AV system. This paper aims to present a review of the state-of-the-art algorithms proposed to enable AV behaviour prediction systems to predict trajectories and intentions for pedestrians and vehicles. This will be achieved by using information from previous literature review papers, recent works, and results obtained using well-known datasets.
作者
我是这篇论文的作者
点击您的名字以认领此论文并将其添加到您的个人资料中。
推荐
暂无数据