Automatic Vehicle-Pedestrian Conflict Identification With Trajectories of Road Users Extracted From Roadside LiDAR Sensors Using a Rule-Based Method

Lv, Bin; Sun, Renjuan; Xu, Hao; Yue, Rui

doi:10.1109/access.2019.2951763

Cited by 30 publications

(12 citation statements)

References 42 publications

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“…Using the RPN in the algorithm model, the height width ratio and area of the detection target can be scaled to generate regional candidate boxes. [20] Although the algorithm can have good image detection effect, the number and quality of the generated region candidate boxes are not ideal, which will have an adverse impact on the detection effect of Faster R-CNN algorithm. [21] In the process of vehicle automatic driving, the traditional Faster R-CNN algorithm can not meet its target detection and surrounding environment judgment, so it needs to be optimized and improved.…”

Section: Optimization and Improvement Strategy Of Msrpnmentioning

confidence: 99%

Research on Vehicle Automatic Driving Target Perception Technology Based on Improved MSRPN Algorithm

Yang

2022

JCCE

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Vehicle automatic driving technology can effectively improve the safety performance of vehicle driving. This research is aimed at the needs of vehicle automatic driving. Combined with vehicle perception technology, a better target recognition algorithm is proposed. By comparing the recognition and recall rate of ION algorithm, HYPERNET algorithm, R-CNN (Regions with CNN features) algorithm and MSRPN algorithm, it can be seen that MSRPN(multi strategy region proposal networks) algorithm has better algorithm performance and is suitable for target detection and recognition in vehicle automatic driving.

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Section: Optimization and Improvement Strategy Of Msrpnmentioning

confidence: 99%

Research on Vehicle Automatic Driving Target Perception Technology Based on Improved MSRPN Algorithm

Yang

2022

JCCE

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“…e protection of VRUs is a common topic in V2P [8]. Advanced Driver Assistance System (ADAS) uses sensor technology [9][10][11], the far-infrared method [12], computer vision [13], and a combination of methods [14] to detect pedestrian location information.…”

Section: Introductionmentioning

confidence: 99%

A V2P Collision Risk Warning Method based on LSTM in IOV

Pan

Jie

Zhang

et al. 2022

Security and Communication Networks

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With the evolution of communication networks, the Internet of Vehicles (IOV) continues to accelerate the safe and rapid development of autonomous vehicles. Vehicle-to-Pedestrian (V2P) communication is a key technology in autonomous vehicles and a potential solution to realize collaborative intelligence between vehicles and pedestrians. However, the existing V2P communication early warning system does not consider the uncertainty of pedestrian trajectory, and the determination of the collision area is limited to a single point, resulting in an inaccurate system judgment and limited improvement of traffic efficiency. This paper designs a new autonomous-oriented V2P communication network architecture and completes a V2P communication early warning system based on Long Range (LoRa). A V2P anticollision model is established, and a new V2P collision risk early warning method is proposed. In this method, danger index is introduced into the early warning of collision between pedestrian and vehicle. The long short-term memory (LSTM) artificial neural network is used to predict the pedestrian’s trajectory, so as to deduce the pedestrian-vehicle collision risk area when the pedestrian trajectory is uncertain. Meanwhile, the confidence probability is used to judge whether the pedestrian and vehicle are warned. The simulation shows that the V2P collision risk warning method proposed in this paper has good performance, which can accurately warn the pedestrian and vehicle under different vehicle speeds and Global Positioning System (GPS) positioning errors. At the same time, it reflects the characteristics of intelligence brought by using LSTM methods. Using the V2P communication early warning system based on LoRa to verify the experimental results show that when the GPS positioning accuracy is submeter level, the prediction accuracy is greater than 98%. The results of the proposed method show good performance and high detection rate.

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“…Z. et al achieved large-area scenario modeling and high-resolution target tracking at intersections using 3D point clouds [ 29 ]. Some authors have also implemented real-time queue range detection [ 30 ] and collision risk analysis [ 31 ] based on roadside lidar. Similarly, the new generation of 79 GHz ultra-bandwidth radar overcomes the lack of angular resolution and is also widely used in ITS.…”

Section: Introductionmentioning

confidence: 99%

Robust Target Detection and Tracking Algorithm Based on Roadside Radar and Camera

Bai

Zhang

et al. 2021

Sensors

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Intelligent transportation systems (ITSs) play an increasingly important role in traffic management and traffic safety. Smart cameras are the most widely used sensors in ITSs. However, cameras suffer from a reduction in detection and positioning accuracy due to target occlusion and external environmental interference, which has become a bottleneck restricting ITS development. This work designs a stable perception system based on a millimeter-wave radar and camera to address these problems. Radar has better ranging accuracy and weather robustness, which is a better complement to camera perception. Based on an improved Gaussian mixture probability hypothesis density (GM-PHD) filter, we also propose an optimal attribute fusion algorithm for target detection and tracking. The algorithm selects the sensors’ optimal measurement attributes to improve the localization accuracy while introducing an adaptive attenuation function and loss tags to ensure the continuity of the target trajectory. The verification experiments of the algorithm and the perception system demonstrate that our scheme can steadily output the classification and high-precision localization information of the target. The proposed framework could guide the design of safer and more efficient ITSs with low costs.

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Automatic Vehicle-Pedestrian Conflict Identification With Trajectories of Road Users Extracted From Roadside LiDAR Sensors Using a Rule-Based Method

Cited by 30 publications

References 42 publications

Research on Vehicle Automatic Driving Target Perception Technology Based on Improved MSRPN Algorithm

Research on Vehicle Automatic Driving Target Perception Technology Based on Improved MSRPN Algorithm

A V2P Collision Risk Warning Method based on LSTM in IOV

Robust Target Detection and Tracking Algorithm Based on Roadside Radar and Camera

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