A Practical Point Cloud Based Road Curb Detection Method for Autonomous Vehicle

Guerrero

2021

Sensors

Curbs are used as physical markers to delimit roads and to redirect traffic into multiple directions (e.g., islands and roundabouts). Detection of road curbs is a fundamental task for autonomous vehicle navigation in urban environments. Since almost two decades, solutions that use various types of sensors, including vision, Light Detection and Ranging (LiDAR) sensors, among others, have emerged to address the curb detection problem. This survey elaborates on the advances of road curb detection problems, a research field that has grown over the last two decades and continues to be the ground for new theoretical and applied developments. We identify the tasks involved in the road curb detection methods and their applications on autonomous vehicle navigation and advanced driver assistance system (ADAS). Finally, we present an analysis on the similarities and differences of the wide variety of contributions.

Section: Ground Segmentationmentioning

confidence: 99%

Section: Tangential Anglementioning

confidence: 99%

Road Curb Detection: A Historical Survey

Guerrero

2021

Sensors

Journal of Advanced Transportation

“…e differential GPS module provided the information on the position, speed, and heading of the ego vehicle. Based on our previous work [36], moving obstacles were detected and tracked by a four-layer laser scanner, which was located at the front of the vehicle. According to the space-time relationship between the moving obstacles, such as pedestrians and vehicles, and experimental vehicles, the position, speed, size, and type of the sports vehicles can be measured.…”

Section: Experimental Platform Constructionmentioning

confidence: 99%

Vehicle Trajectory Prediction by Knowledge-Driven LSTM Network in Urban Environments

Wang

Zhao

et al. 2020

Self Cite

An accurate prediction of future trajectories of surrounding vehicles can ensure safe and reasonable interaction between intelligent vehicles and other types of vehicles. Vehicle trajectories are not only constrained by a priori knowledge about road structure, traffic signs, and traffic rules but also affected by posterior knowledge about different driving styles of drivers. The existing prediction models cannot fully combine the prior and posterior knowledge in the driving scene and perform well only in a specific traffic scenario. This paper presents a long short-term memory (LSTM) neural network driven by knowledge. First, a driving knowledge base is constructed to describe the prior knowledge about a driving scenario. Then, the prediction reference baseline (PRB) based on driving knowledge base is determined by using the rule-based online reasoning system. Finally, the future trajectory of the target vehicle is predicted by an LSTM neural network based on the prediction reference baseline, while the predicted trajectory considers both posterior and prior knowledge without increasing the computation complexity. The experimental results show that the proposed trajectory prediction model can adapt to different driving scenarios and predict trajectories with high accuracy due to the unique combination of the prior and posterior knowledge in the driving scene.

“…Moras et al presented an occupancy grid framework that generates a global static map and classifies local moving objects simultaneously [11,12,13]. Classification of traffic objects (such as vehicles, pedestrians, road curbs, and poles) is used to classify the motion of a point cloud [14,15,16].…”

Section: Previous Studiesmentioning

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

Sensors

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.