Novel Intersection Type Recognition for Autonomous Vehicles Using a Multi-Layer Laser Scanner

Understanding the scene in front of a vehicle is crucial for self-driving vehicles and Advanced Driver Assistance Systems, and in urban scenarios, intersection areas are one of the most critical, concentrating between 20% to 25% of road fatalities. This research presents a thorough investigation on the detection and classification of urban intersections as seen from onboard front-facing cameras. Different methodologies aimed at classifying intersection geometries have been assessed to provide a comprehensive evaluation of state-of-the-art techniques based on Deep Neural Network (DNN) approaches, including single-frame approaches and temporal integration schemes. A detailed analysis of most popular datasets previously used for the application together with a comparison with ad hoc recorded sequences revealed that the performances strongly depend on the field of view of the camera rather than other characteristics or temporal-integrating techniques. Due to the scarcity of training data, a new dataset is created by performing data augmentation from real-world data through a Generative Adversarial Network (GAN) to increase generalizability as well as to test the influence of data quality. Despite being in the relatively early stages, mainly due to the lack of intersection datasets oriented to the problem, an extensive experimental activity has been performed to analyze the individual performance of each proposed systems.

Section: Related Workwordmentioning

confidence: 99%

Urban Intersection Classification: A Comparative Analysis

Ballardini

Hernández

Carrasco

et al. 2021

“…To characterize the local properties of the objects of interest, each cluster is divided into 4 layers and most features are computed in each of 4 layers. This subdivision method can provide a more flexible classification representation for the occluded object [23]. Apart from the previous features in literatures, some new features are proposed to develop the pedestrian cues.…”

Section: Feature Collectionmentioning

confidence: 99%

An Approach to Segment and Track-Based Pedestrian Detection from Four-Layer Laser Scanner Data

Zhang

Cheng

et al. 2019

Pedestrian detection is a critical perception task for autonomous driving and intelligent vehicle, and it is challenging due to the potential variation of appearance and pose of human beings as well as the partial occlusion. In this paper, we present a novel pedestrian detection method via four-layer laser scanner. The proposed approach deals with the occlusion problem by fusing the segment classification results with past knowledge integration from tracking process. First, raw point cloud is segmented into the clusters of independent objects. Then, three types of features are proposed to capture the comprehensive cues, and 18 effective features are extracted with the combination of the univariate feature selection algorithm and feature correlation analysis process. Next, based on the segment classification at individual frame, the track classification is conducted further for consecutive frames using particle filter and probability data association filter. Experimental results demonstrate that both back-propagation neural network and Adaboost classifiers based on 18 selected features have their own advantages at the segment classification stage in terms of pedestrian detection performance and computation time, and the track classification procedure can improve the detection performance particularly for partially occluded pedestrians in comparison with the single segment classification procedure.

“…For instance, points classified as static are measured from the surfaces of static objects, such as curbs, poles, buildings, and parked vehicles. Such a static point cloud can be applied to various automated and intelligent driving functions, such as mapping, localization, and collision avoidance systems [3,4,5]. Points classified as dynamic are detected from objects that have speeds above a certain level, such as nearby moving vehicles, motorcycles, and pedestrians.…”

Section: Introductionmentioning

confidence: 99%

Rapid Motion Segmentation of LiDAR Point Cloud Based on a Combination of Probabilistic and Evidential Approaches for Intelligent Vehicles

Lee

Kim

et al. 2019

Point clouds from light detecting and ranging (LiDAR) sensors represent increasingly important information for environmental object detection and classification of automated and intelligent vehicles. Objects in the driving environment can be classified as either dynamic or static depending on their movement characteristics. A LiDAR point cloud is also segmented into dynamic and static points based on the motion properties of the measured objects. The segmented motion information of a point cloud can be useful for various functions in automated and intelligent vehicles. This paper presents a fast motion segmentation algorithm that segments a LiDAR point cloud into dynamic and static points in real-time. The segmentation algorithm classifies the motion of the latest point cloud based on the LiDAR’s laser beam characteristics and the geometrical relationship between consecutive LiDAR point clouds. To accurately and reliably estimate the motion state of each LiDAR point considering the measurement uncertainty, both probability theory and evidence theory are employed in the segmentation algorithm. The probabilistic and evidential algorithm segments the point cloud into three classes: dynamic, static, and unknown. Points are placed in the unknown class when LiDAR point cloud is not sufficient for motion segmentation. The point motion segmentation algorithm was evaluated quantitatively and qualitatively through experimental comparisons with previous motion segmentation methods.