Recognition of Lane Changing Maneuvers for Vehicle Driving Safety

Wu, Yuming; Zhang, Lei; Lou, Ren; Li, Xinghua

doi:10.3390/electronics12061456

Cited by 4 publications

(5 citation statements)

References 25 publications

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“…This approach is particularly valuable for challenging data collection scenarios, such as with heavy-duty freight vehicles. Wu et al [1] present a model for identifying lane-changing maneuvers using the HighD dataset. Focusing on acceleration and velocity as physical data, the research implements a k-nearest neighbor (KNN) classification model.…”

Section: Driver Maneuver Recognitionmentioning

confidence: 99%

“…Two ablation experiments were performed: (1) Comparison between the improved cross-entropy loss function (ICELF) and the regular cross-entropy loss function, assessing the accuracy of driver intent recognition results under these two scenarios. (2) Comparison of the accuracy of driver intent recognition results obtained by adding the CSAM and MCFM against directly using CSAM4.…”

Section: Ablation Experimentsmentioning

confidence: 99%

“…Research on road safety indicates that the majority of traffic accidents stem from improper driver maneuvers. Despite achieving satisfactory performance in specific scenarios, fully autonomous driving remains a long-term objective due to the ongoing necessity for comprehensive legislation, regulations, and infrastructure development [1]. Consequently, human-machine cooperative driving continues to be a crucial research direction in intelligent transportation systems.…”

Section: Introductionmentioning

confidence: 99%

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STA-Net: A Spatial–Temporal Joint Attention Network for Driver Maneuver Recognition, Based on In-Cabin and Driving Scene Monitoring

He,

Yu,

Wang

et al. 2024

Applied Sciences

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Next-generation advanced driver-assistance systems (ADASs) are a promising direction for intelligent transportation systems. To achieve intelligent security monitoring, it is imperative that vehicles possess the ability to accurately comprehend driver maneuvers amidst diverse driver behaviors and complex driving scenarios. Existing CNN-based and transformer-based driver maneuver recognition methods face challenges in effectively capturing global and local features across temporal and spatial dimensions. This paper proposes a Spatial–Temporal Joint Attention Network (STA-Net) to realize high-efficient temporal and spatial feature extractions in driver maneuver recognition. First, we introduce a two-stream architecture for a concurrent analysis of in-cabin driver behaviors and out-cabin environmental information. Second, we propose a Multi-Scale Transposed Attention (MSTA) module and Multi-Scale Feedforward Network (MSFN) to extract features at multiple scales, addressing receptive field inadequacies and combining high-level and low-level information. Third, to address the information redundancy in multi-scale features, we propose a Cross-Spatial Attention Module (CSAM) and Multi-Scale Cross-Spatial Fusion Module (MCFM) to select essential features. Additionally, we introduce an asymmetric loss function to effectively tackle the issue of sample imbalance across diverse categories of driving maneuvers. The proposed method demonstrates a remarkable accuracy of 90.97% and an F1 score of 89.37% on the Brain4Cars dataset, surpassing the performance of the methods compared. These results substantiate the fact that our approach effectively enhances driver maneuver recognition.

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Section: Driver Maneuver Recognitionmentioning

confidence: 99%

Section: Ablation Experimentsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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STA-Net: A Spatial–Temporal Joint Attention Network for Driver Maneuver Recognition, Based on In-Cabin and Driving Scene Monitoring

He,

Yu,

Wang

et al. 2024

Applied Sciences

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“…Shao [11] improved the accuracy of vehicle detection using YOLOv5 and increased the convergence rate. Wu [12] identified lane changing manoeuvres of vehicles through a distinct set of physical data such as acceleration or speed.…”

Section: Related Workmentioning

confidence: 99%

A Comparative Analysis of Cross-Validation Techniques for a Smart and Lean Pick-and-Place Solution with Deep Learning

et al. 2023

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As one of the core applications of computer vision, object detection has become more important in scenarios requiring high accuracy but with limited computational resources such as robotics and autonomous vehicles. Object detection using machine learning running on embedded device such as Raspberry Pi provides the high possibility to detect any custom objects without the recalibration of camera. In this work, we developed a smart and lean object detection model for shipping containers by using the state-of-the-art deep learning TensorFlow model and deployed it to a Raspberry Pi. Using EfficientDet-Lite2, we explored the different cross-validation strategies (Hold-out and K-Fold). The experimental results show that compared with the baseline EfficientDet-Lite2 algorithm, our model improved the mean average precision (mAP) by 44.73% for the Hold-out dataset and 6.26% for K-Fold cross-validation. We achieved Average Precision (AP) of more than 80% and best detection scores of more than 93% for the Hold-out dataset. For the 5-Fold lean dataset, the results show the Average Precision across the three lightweight models are generally high as the models achieved more than 50% average precision, with YOLOv4 Tiny performing better than EfficientDet-Lite2 and Single Shot Detector (SSD) MobileNet V2 Feature Pyramid Network (FPN) 320 as a lightweight model.

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“…While driving on the road, AV performs multiple operations such as lane change, lane keeping, overtaking, and following the traffic rules. Several studies proposed and developed numerous methods for ADAS systems [31,32]. It is equally important for an autonomous vehicle to be aware of the textual cues appearing in its outer environment to take some decision or assist the driver, either to stop or drive.…”

Section: Introductionmentioning

confidence: 99%

An Intelligent System to Sense Textual Cues for Location Assistance in Autonomous Vehicles

Unar

Liu

et al. 2023

Sensors

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The current technological world is growing rapidly and each aspect of life is being transformed toward automation for human comfort and reliability. With autonomous vehicle technology, the communication gap between the driver and the traditional vehicle is being reduced through multiple technologies and methods. In this regard, state-of-the-art methods have proposed several approaches for advanced driver assistance systems (ADAS) to meet the requirement of a level-5 autonomous vehicle. Consequently, this work explores the role of textual cues present in the outer environment for finding the desired locations and assisting the driver where to stop. Firstly, the driver inputs the keywords of the desired location to assist the proposed system. Secondly, the system will start sensing the textual cues present in the outer environment through natural language processing techniques. Thirdly, the system keeps matching the similar keywords input by the driver and the outer environment using similarity learning. Whenever the system finds a location having any similar keyword in the outer environment, the system informs the driver, slows down, and applies the brake to stop. The experimental results on four benchmark datasets show the efficiency and accuracy of the proposed system for finding the desired locations by sensing textual cues in autonomous vehicles.

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Recognition of Lane Changing Maneuvers for Vehicle Driving Safety

Cited by 4 publications

References 25 publications

STA-Net: A Spatial–Temporal Joint Attention Network for Driver Maneuver Recognition, Based on In-Cabin and Driving Scene Monitoring

STA-Net: A Spatial–Temporal Joint Attention Network for Driver Maneuver Recognition, Based on In-Cabin and Driving Scene Monitoring

A Comparative Analysis of Cross-Validation Techniques for a Smart and Lean Pick-and-Place Solution with Deep Learning

An Intelligent System to Sense Textual Cues for Location Assistance in Autonomous Vehicles

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