A Point Cloud-Based Robust Road Curb Detection and Tracking Method

Wang, Guojun; Wu, Jian; He, Rui; Yang, Shuying

doi:10.1109/access.2019.2898689

Cited by 65 publications

(39 citation statements)

References 27 publications

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“…Another study was conducted on road edge detection of structured roads based on lidar alone. In [ 29 ], Wang et al proposed a method for road edge detection of structured roads based on robust 3D-lidar.…”

Section: Related Workmentioning

confidence: 99%

End-to-End Automated Lane-Change Maneuvering Considering Driving Style Using a Deep Deterministic Policy Gradient Algorithm

Wang

et al. 2020

Sensors

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Changing lanes while driving requires coordinating the lateral and longitudinal controls of a vehicle, considering its running state and the surrounding environment. Although the existing rule-based automated lane-changing method is simple, it is unsuitable for unpredictable scenarios encountered in practice. Therefore, using a deep deterministic policy gradient (DDPG) algorithm, we propose an end-to-end method for automated lane changing based on lidar data. The distance state information of the lane boundary and the surrounding vehicles obtained by the agent in a simulation environment is denoted as the state space for an automated lane-change problem based on reinforcement learning. The steering wheel angle and longitudinal acceleration are used as the action space, and both the state and action spaces are continuous. In terms of the reward function, avoiding collision and setting different expected lane-changing distances that represent different driving styles are considered for security, and the angular velocity of the steering wheel and jerk are considered for comfort. The minimum speed limit for lane changing and the control of the agent for a quick lane change are considered for efficiency. For a one-way two-lane road, a visual simulation environment scene is constructed using Pyglet. By comparing the lane-changing process tracks of two driving styles in a simplified traffic flow scene, we study the influence of driving style on the lane-changing process and lane-changing time. Through the training and adjustment of the combined lateral and longitudinal control of autonomous vehicles with different driving styles in complex traffic scenes, the vehicles could complete a series of driving tasks while considering driving-style differences. The experimental results show that autonomous vehicles can reflect the differences in the driving styles at the time of lane change at the same speed. Under the combined lateral and longitudinal control, the autonomous vehicles exhibit good robustness to different speeds and traffic density in different road sections. Thus, autonomous vehicles trained using the proposed method can learn an automated lane-changing policy while considering safety, comfort, and efficiency.

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Section: Related Workmentioning

confidence: 99%

End-to-End Automated Lane-Change Maneuvering Considering Driving Style Using a Deep Deterministic Policy Gradient Algorithm

Wang

et al. 2020

Sensors

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“…The change in the data position of the point cloud due to the movement of the vehicle affects the boundary extraction of the road. To solve this problem, we use the method of Wang G et al [6] to process the original point cloud data. This method obtains the posture and position of the mining truck at a certain moment by the position and posture between two adjacent frames.…”

Section: Data Preprocessingmentioning

confidence: 99%

“…The previous point has a larger impact on the actual detection of the road, thus the value of n is greater than m. Finally, the candidate points are fitted and filtered using the RANSAC algorithm. The least squares fitting is to fit all points including noise points [6]. RANSAC estimated polynomial model avoids noise fitting through multiple iterations.…”

Section: Boundary Points Fittingmentioning

confidence: 99%

“…However, this method does not use the results of road boundary point tracking to improve the results of road detection. In [6], KF is also used for road curb tracking. The method uses an Amplitude-Limiting filter to determine whether the predicted point and the current difference satisfy the threshold.…”

Section: Road Boundary Trackingmentioning

confidence: 99%

“…Wang et al [5] proposed a double layer beam model method to effectively detect intersection roads. This method [6] effectively solves problems such as road discontinuities, obstructions, and corners. Due to the simple structure of the urban road, the above method is applicable to urban scenes and not to mine roads.…”

Section: Introductionmentioning

confidence: 99%

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Real-Time Mine Road Boundary Detection and Tracking for Autonomous Truck

Ai²,

Tian

2020

Sensors

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Road boundary detection is an important part of the perception of the autonomous driving. It is difficult to detect road boundaries of unstructured roads because there are no curbs. There are no clear boundaries on mine roads to distinguish areas within the road boundary line and areas outside the road boundary line. This paper proposes a real-time road boundary detection and tracking method by a 3D-LIDAR sensor. The road boundary points are extracted from the detected elevated point clouds above the ground point cloud according to the spatial distance characteristics and the angular features. Road tracking is to predict and update the boundary point information in real-time, in order to prevent false and missed detection. The experimental verification of mine road data shows the accuracy and robustness of the proposed algorithm.

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