期刊
IEEE ROBOTICS AND AUTOMATION LETTERS
卷 6, 期 2, 页码 1662-1669出版社
IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
DOI: 10.1109/LRA.2021.3059628
关键词
AI-based methods; autonomous vehicle navigation; intelligent transportation systems; learning from demonstration
类别
资金
- SK hynix Inc.
This study introduces a novel deep neural network that can predict traversability maps for autonomous driving in a dynamic environment considering multiple contexts without using expensive prior information. Experimental results demonstrate that the proposed method improves prediction accuracy and can predict future trajectories similar to those followed by a human driver.
Autonomous driving in an urban environment with surrounding agents remains challenging. One of the key challenges is to accurately predict the traversability map that probabilistically represents future trajectories considering multiple contexts: inertial, environmental, and social. To address this, various approaches have been proposed; however, they mainly focus on considering the individual context. In addition, most studies utilize expensive prior information (such as HD maps) of the driving environment, which is not a scalable approach. In this study, we extend a deep inverse reinforcement learning-based approach that can predict the traversability map while incorporating multiple contexts for autonomous driving in a dynamic environment. Instead of using expensive prior information of the driving scene, we propose a novel deep neural network to extract contextual cues from sensing data and effectively incorporate them in the output, i.e., the reward map. Based on the reward map, our method predicts the ego-centric traversability map that represents the probability distribution of the plausible and socially acceptable future trajectories. The proposed method is qualitatively and quantitatively evaluated in real-world traffic scenarios with various baselines. The experimental results show that our method improves the prediction accuracy compared to other baseline methods and can predict future trajectories similar to those followed by a human driver.
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