3D object detection using point cloud is an essential task for autonomous driving. With the development of infrastructures, roadside perception can extend the view range of the autonomous vehicles through communication technology. Computation time and power consumption are two main concerns when deploying object detection tasks, and a light-weighted detection model applied in an embedded system is a convenient solution for both roadside and vehicleside. In this study, a 3D Point cLoud Object deTection (PLOT) network is proposed to reduce heavy computing and ensure real-time object detection performance in an embedded system. First, a bird’s eye view representation of the point cloud is calculated using pillar-based encoding method. Then a cross-stage partial network-based backbone and a feature pyramid network-based neck are implemented to generate the high-dimensional feature map. Finally, a multioutput head using a shared convolutional layer is attached to predict classes, bounding boxes, and the orientations of the objects at the same time. Extensive experiments using the Waymo Open Dataset and our own dataset are conducted to demonstrate the accuracy and efficiency of the proposed method.

Real-time localization is an important mission for self-driving cars and it is difficult to achieve precise pose information in dynamic environments. In this paper, a novel localization method is proposed to estimate the pose of self-driving cars using a 3D-LiDAR sensor. First, the multi-frame curb features and laser intensity features are extracted. Meanwhile, based on the high-precision curb map generated offline, obstacles on road are detected using region segmentation methods and their features are removed. Furthermore, a map-matching method is proposed to match the features to the map, a robust iterative closest point algorithm is utilized to deal with curb features along with a probability search method dealing with intensity features. Finally, two separate Kalman filters are used to fuse the low-cost global positioning systems and map-matching results. Both offline and online experiments are carried out in dynamic environments and the results demonstrate the accuracy and robustness of the proposed method.

Yushu Prefecture in Qinghai Province provides some of the largest known stretches of habitat for the Vulnerable snow leopard Panthera uncia in China. People living in these areas are dependent on agropastoralism. Support from local communities is necessary for effective long-term conservation action for snow leopards, but loss of livestock to snow leopards can create financial burdens that induce negative attitudes and encourage retaliatory killing. We assessed factors driving herders' attitudes towards snow leopards and their conservation. We found that herders had higher agreement with positive than with negative statements about snow leopards despite nearly half reporting livestock loss to snow leopards within the last 5 years. No retaliatory killing was reported. Herders with more years of formal education and fewer livestock losses were more likely to have positive attitudes whereas those with lower importance of snow leopards to their religion, fewer livestock losses, and fewer years of education were more likely to have negative attitudes. Understanding the multifaceted mechanisms responsible for positive views towards species is imperative for reaching conservation goals. Our findings ascribe to the importance of increased education and adherence to Tibetan beliefs in promoting conservation tolerance towards snow leopards in Qinghai Province, but also indicate a need for further research into the impact of livestock loss.