College of Information Science and Engineering, Ritsumeikan University, 1-1-1, Noji-higashi, Kusatsu 525-8577, Shiga, Japan.
Department of Aeronautical and Aviation Engineering, The Hong Kong Polytechnic University, 11 Yuk Choi Rd, Hung Hom, Kowloon, Hong Kong.
Sensors (Basel). 2023 Mar 14;23(6):3096. doi: 10.3390/s23063096.
It is crucial for an autonomous vehicle to predict cyclist behavior before decision-making. When a cyclist is on real traffic roads, his or her body orientation indicates the current moving directions, and his or her head orientation indicates his or her intention for checking the road situation before making next movement. Therefore, estimating the orientation of cyclist's body and head is an important factor of cyclist behavior prediction for autonomous driving. This research proposes to estimate cyclist orientation including both body and head orientation using deep neural network with the data from Light Detection and Ranging (LiDAR) sensor. In this research, two different methods are proposed for cyclist orientation estimation. The first method uses 2D images to represent the reflectivity, ambient and range information collected by LiDAR sensor. At the same time, the second method uses 3D point cloud data to represent the information collected from LiDAR sensor. The two proposed methods adopt a model ResNet50, which is a 50-layer convolutional neural network, for orientation classification. Hence, the performances of two methods are compared to achieve the most effective usage of LiDAR sensor data in cyclist orientation estimation. This research developed a cyclist dataset, which includes multiple cyclists with different body and head orientations. The experimental results showed that a model that uses 3D point cloud data has better performance for cyclist orientation estimation compared to the model that uses 2D images. Moreover, in the 3D point cloud data-based method, using reflectivity information has a more accurate estimation result than using ambient information.
对于自动驾驶汽车来说，在做出决策之前预测自行车手的行为至关重要。当自行车手在真实交通道路上时，他或她的身体朝向指示当前的移动方向，而他或她的头部朝向指示他或她在做出下一步动作之前检查道路情况的意图。因此，估计自行车手的身体和头部的朝向是自动驾驶中自行车手行为预测的一个重要因素。本研究提出使用基于激光雷达（LiDAR）传感器数据的深度神经网络来估计包括身体和头部朝向的自行车手的朝向。在本研究中，提出了两种用于自行车手朝向估计的不同方法。第一种方法使用 2D 图像来表示由 LiDAR 传感器收集的反射率、环境和距离信息。同时，第二种方法使用 3D 点云数据来表示从 LiDAR 传感器收集的信息。这两种方法采用的是 50 层卷积神经网络 ResNet50 进行朝向分类。因此，比较了两种方法的性能，以实现 LiDAR 传感器数据在自行车手朝向估计中的最有效利用。本研究开发了一个自行车手数据集，其中包括具有不同身体和头部朝向的多个自行车手。实验结果表明，与使用 2D 图像的模型相比，使用 3D 点云数据的模型在自行车手朝向估计方面具有更好的性能。此外，在基于 3D 点云数据的方法中，使用反射率信息比使用环境信息具有更准确的估计结果。
