Vehicle Trajectory Prediction Method Based on License Plate Information Obtained from Video-Imaging Detectors in Urban Road Environment

Zhang, Zheng; Liu, Haiqing; Rai, Laxmisha; Zhang, Siyi

doi:10.3390/s20051258

Cited by 14 publications

(6 citation statements)

References 22 publications

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“…The license plate information can be applied to barrier access control [ 20 , 22 ], vehicle target detection [ 49 ], vehicle re-identification [ 50 ], etc. Moreover, the location of the license plate can be used for vehicle trajectory prediction [ 51 ] via license plate detection and tracking.…”

Section: Discussionmentioning

confidence: 99%

Scale-Invariant Multidirectional License Plate Detection with the Network Combining Indirect and Direct Branches

Chen

Liu

et al. 2021

Sensors

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As the license plate is multiscale and multidirectional in the natural scene image, its detection is challenging in many applications. In this work, a novel network that combines indirect and direct branches is proposed for license plate detection in the wild. The indirect detection branch performs small-sized vehicle plate detection with high precision in a coarse-to-fine scheme using vehicle–plate relationships. The direct detection branch detects the license plate directly in the input image, reducing false negatives in the indirect detection branch due to the miss of vehicles’ detection. We propose a universal multidirectional license plate refinement method by localizing the four corners of the license plate. Finally, we construct an end-to-end trainable network for license plate detection by combining these two branches via post-processing operations. The network can effectively detect the small-sized license plate and localize the multidirectional license plate in real applications. To our knowledge, the proposed method is the first one that combines indirect and direct methods into an end-to-end network for license plate detection. Extensive experiments verify that our method outperforms the indirect methods and direct methods significantly.

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Section: Discussionmentioning

confidence: 99%

Scale-Invariant Multidirectional License Plate Detection with the Network Combining Indirect and Direct Branches

Chen

Liu

et al. 2021

Sensors

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“…Therefore, the core of this type of approach is to develop a way to describe this goal, no matter with, by setting a hand-crafted cost function or inferring it from observations. For example, Zhang et al (2020) developed a Dijkstra-based trip chain compensation method to predict real-time vehicle trajectory using the historical trip rules extracted from vehicle license plate data in an urban road environment. Li et al (2017) extended the original generative adversarial imitation learning (GAIL) approach (Ho & Ermon, 2016) by adding an information-aware element to the loss function.…”

Section: Related Workmentioning

confidence: 99%

“…Over the last decade, the field of autonomous driving has experienced unprecedented growth, resulting in the integration of various entry-level automation techniques, such as Advanced Driver-Assistance Systems and Traffic Jam Pilot, into newly manufactured vehicles (Chen et al, 2020). The implementation of these technologies has demonstrated improvements in road capacity and average driving speeds, and significant reductions in traffic accidents and exhaust emissions (Li et al, 2019b).…”

Section: Introductionmentioning

confidence: 99%

Context‐aware trajectory prediction for autonomous driving in heterogeneous environments

Chen

Li³

et al. 2023

Computer aided Civil Eng

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The prediction of surrounding agent trajectories in heterogeneous traffic environments remains a challenging task for autonomous driving due to several critical issues, such as understanding social interactions among agents and the environment, handling multiclass traffic movements, and generating feasible trajectories in accordance with real‐world rules, all of which hinder prediction accuracy. To address these issues, a new multimodal trajectory prediction framework based on the transformer network is presented in this study. A hierarchical‐structured context‐aware module, inspired by human perceptual logic, is proposed to capture contextual information within the scene. An efficient linear global attention mechanism is also proposed to reduce the computation and memory load of the transformer framework. Additionally, this study introduces a novel auxiliary loss to penalize infeasible off‐road predictions. Empirical results on the Lyft l5kit data set demonstrate the state‐of‐the‐art performance of the proposed model, which substantially enhances the accuracy and feasibility of prediction outcomes. The proposed model also possesses a unique feature, effectively dealing with missing input observations. This study underscores the importance of comprehending social interactions among agents and the environment, handling multiclass traffic movements, and generating feasible trajectories adhering to real‐world rules in autonomous driving.

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“…The work done in [ 10 ] relied on the license plate data obtained from the massive volume of information collected from video imaging to predict vehicle trajectory. The paper proposed a real-time vehicle trajectory prediction method based on (i) historical trip rules extracted from vehicle license plate data in an urban road environment; (ii) vehicle trip chain acquired on the basis of the topologic graph of the road network, channelization of intersections, and the driving status information at intersections; (iii) a trip chain compensation method based on the Dijkstra algorithm to complement missing data in the original vehicle license plate.…”

Section: Autonomy and Path Planningmentioning

confidence: 99%

Intelligent Transportation Related Complex Systems and Sensors

Kyamakya

Chedjou

Machot

et al. 2021

Sensors

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Vehicle Trajectory Prediction Method Based on License Plate Information Obtained from Video-Imaging Detectors in Urban Road Environment

Cited by 14 publications

References 22 publications

Scale-Invariant Multidirectional License Plate Detection with the Network Combining Indirect and Direct Branches

Scale-Invariant Multidirectional License Plate Detection with the Network Combining Indirect and Direct Branches

Context‐aware trajectory prediction for autonomous driving in heterogeneous environments

Intelligent Transportation Related Complex Systems and Sensors

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