# When Intelligent Transportation Systems Sensing Meets Edge Computing: Vision and Challenges
## 3 ITS Sensing
### LiDAR and Camera  
LiDAR has been predominately used in autonomous vehicles compared to its use in transportation infrastructure systems. LiDAR signal is 3D point cloud and it can be used for 3D object detection, 3D object tracking, lane detection, obstacle detection, traffic sign detection, and 3D mapping in autonomous vehicles' perception systems [81]. For example, [PERSON] et al. proposed PointNets, a deep learning framework for 3D object detection from RGB-D data that learned directly from the raw point clouds to extract 3D bounding boxes of vehicles [82]. [PERSON] et al. proposed using machine learning for combined LiDAR/stereo vision data that did tracking and obstacle detection at the same time [83]. [PERSON] et al. designed an expectation-maximization-based method for real-time 3D road lane detection using raw LiDAR signals from a probe vehicle [84]. [PERSON] developed a traffic sign classifier based on a supervised Gaussian-Bernoulli deep Boltzmann machine model, which used LiDAR point cloud and images as input [85]. There are also some representative works providing critical insights into the application of LiDAR as an infrastructure-based sensor. [PERSON] et al. proposed a clustering method for detecting and tracking pedestrians and vehicles using roadside LiDAR [18]. The findings are helpful for both researchers and transportation engineers.  
Camera collects images or videos, and these raw data are essentially 2D matrices with quantized pixel numbers that are samples of the real-world visual signals. New techniques are applied to convert these complex 2D matrices into traffic-related data. One fundamental application is object detection. Researchers in the engineering and computer science fields have spent a lot of effort designing smart and fast object detectors using traditional statistics/learning [15] and deep learning techniques [86; 87]. Object detection localizes and classifies cars, trucks, pedestrians, bicyclists, etc., in traffic camera images and enables different data collection tasks. There are also datasets being collected and published specifically for object detection and classification in traffic surveillance images, which has generated much interest [88]. The AI City Challenge is a leading workshop and competition in the field of traffic surveillance video data processing [89]. It has guided video-based traffic sensing, such as traffic volume counting, vehicle re-identification, multiple-vehicle tracking, and traffic anomaly detection. Since the camera sensor is a critical component of autonomous vehicles, advanced traffic sensing techniques have been heavily deployed in autonomous vehicles' perception systems to understand the environment [81]. Traffic events, congestion levels, road users, road regions, infrastructure information, road user interactions, etc., are all meaningful data that can be extracted from the raw images using machine learning. Datasets have also been published and widely recognized to facilitate the design of cutting-edge methods for converting camera data into readable traffic-related data [90; 91; 92; 93].