Vision-Based Real-Time Online Vulnerable Traffic Participants Trajectory Prediction for Autonomous Vehicle

Chen, Hao; Liu, Yinhua; Zhao, Baixuan; Hu, Chuan; Zhang, Xi

doi:10.1109/tiv.2022.3227940

Cited by 14 publications

(3 citation statements)

References 43 publications

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“…Among the agent-specific input features, the 2D position of each agent, viewed from a bird's eye view (BEV) perspective, is the most prevalent feature employed by almost all trajectory prediction methods [16,20,27,30,37,42,54,61,67,69,72,84,91,96,[100][101][102][103][104][105]. This method uses the 2D position with global or local scene coordinates depending on its application.…”

Section: Possible Input Featuresmentioning

confidence: 99%

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A Review of Trajectory Prediction Methods for the Vulnerable Road User

Schuetz,

Flohr

2023

Robotics

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Predicting the trajectory of other road users, especially vulnerable road users (VRUs), is an important aspect of safety and planning efficiency for autonomous vehicles. With recent advances in Deep-Learning-based approaches in this field, physics- and classical Machine-Learning-based methods cannot exhibit competitive results compared to the former. Hence, this paper provides an extensive review of recent Deep-Learning-based methods in trajectory prediction for VRUs and autonomous driving in general. We review the state and context representations and architectural insights of selected methods, divided into categories according to their primary prediction scheme. Additionally, we summarize reported results on popular datasets for all methods presented in this review. The results show that conditional variational autoencoders achieve the best overall results on both pedestrian and autonomous driving datasets. Finally, we outline possible future research directions for the field of trajectory prediction in autonomous driving.

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Section: Possible Input Featuresmentioning

confidence: 99%

“…Many trajectory prediction methods utilize a simple scene representation strategy where the 2D position of agents is used without additional processing [20,26,69,74,102]. However, increasingly, researchers are exploring the use of graph-based scene representations.…”

Section: Scene Representation and Interaction Modelingmentioning

confidence: 99%

A Review of Trajectory Prediction Methods for the Vulnerable Road User

Schuetz,

Flohr

2023

Robotics

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“…Computer vision technology has undergone rapid development and has been widely applied in various domains of life [1,2], especially in the field of transportation [3]. This technology enables the identification of vehicles in single-camera scenes, allowing for the extraction of vehicle information such as speed, location, trajectory, and license plate [4,5].…”

Section: Introductionmentioning

confidence: 99%

Multi-Adjacent Camera-Based Dangerous Driving Trajectory Recognition for Ultra-Long Highways

Zhao,

Fu,

Yang

et al. 2024

Applied Sciences

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Fast detection of the trajectory is the key point to improve the further emergency proposal. Especially for ultra-long highway, prompt detection is labor-intensive. However, automatic detection relies on the accuracy and speed of vehicle detection, and tracking. In multi-camera surveillance system for ultra-long highways, it is often difficult to capture the same vehicle without intervals, which makes vehicle re-recognition crucial as well. In this paper, we present a framework that includes vehicle detection and tracking using improved DeepSORT, vehicle re-identification, feature extraction based on trajectory rules, and behavior recognition based on trajectory analysis. In particular, we design a network architecture based on DeepSORT with YOLOv5s to address the need for real-time vehicle detection and tracking in real-world traffic management. We further design an attribute recognition module to generate matching individuality attributes for vehicles to improve vehicle re-identification performance under multiple neighboring cameras. Besides, the use of bidirectional LSTM improves the accuracy of trajectory prediction, demonstrating its robustness to noise and fluctuations. The proposed model has a high advantage from the cumulative matching characteristic (CMC) curve shown and even improves above 15.38% compared to other state-of-the-art methods. The model developed on the local highway vehicle dataset is comprehensively evaluated, including abnormal trajectory recognition, lane change detection, and speed anomaly recognition. Experimental results demonstrate the effectiveness of the proposed method in accurately identifying various vehicle behaviors, including lane changes, stops, and even other dangerous driving behavior.

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Insights into Travel Pattern Analysis and Demand Prediction: A Data-Driven Approach in Bike-Sharing Systems

Lin,

He,

et al. 2024

J. Transp. Eng., Part A: Systems

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Vision-Based Real-Time Online Vulnerable Traffic Participants Trajectory Prediction for Autonomous Vehicle

Cited by 14 publications

References 43 publications

A Review of Trajectory Prediction Methods for the Vulnerable Road User

A Review of Trajectory Prediction Methods for the Vulnerable Road User

Multi-Adjacent Camera-Based Dangerous Driving Trajectory Recognition for Ultra-Long Highways

Insights into Travel Pattern Analysis and Demand Prediction: A Data-Driven Approach in Bike-Sharing Systems

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