Overtaking &amp;amp; receding vehicle detection for driver assistance and naturalistic driving studies

Satzoda, Ravi Kumar; Trivedi, Mohan M.

doi:10.1109/itsc.2014.6957771

Cited by 13 publications

(4 citation statements)

References 20 publications

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“…Petrov et al [6] modeled autonomous vehicle overtaking, and proposed a nonlinear control scheme that uses only the relative position and orientation with respect to the overtaken vehicle acquired from onboard sensors. Other studies such as [7] contributed to the subject by proposing a vehicle detection assistance system, that uses appearances to detect overtaking and receding vehicles. Concerning the actual maneuvers themselves, different control laws were proposed to govern the maneuvers in autonomous vehicles ( [8], [9]).…”

Section: Related Workmentioning

confidence: 99%

Cooperative Lateral Maneuvers Manager for Multi-autonomous Vehicles

Assaad

Talj

Charara

2018

2018 IEEE International Conference on Systems, Man, and Cybernetics (SMC)

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Vehicle automation opened the door for a myriad of new applications that target vehicles. Longitudinal maneuvers are well studied in the literature, whereas lateral maneuvers studies are scarce in comparison. Maneuvers such as lane changing on highways are complex, and require gathering information about the different neighbors in the vicinity of the subject vehicle. Vehicle-to-vehicle (V2V) communications provided new ways for acquiring such information. Cooperation is proven to be useful when performing such maneuvers. Using V2V communication, and Systems of Systems (SoS) principles, this paper presents a Cooperative Lateral Maneuvers Manager (CLMM), an integrated system in autonomous vehicles, that implements the requirements of the Cooperative maneuvers manager for autonomous vehicles (CMMAV), a SoS that enables cooperation between autonomous vehicles to perform their desired lateral maneuvers. CLMM was validated using Anylogic * , a multi-agent simulator, and the results show that it respects the requirements of the system.

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Section: Related Workmentioning

confidence: 99%

Cooperative Lateral Maneuvers Manager for Multi-autonomous Vehicles

Assaad

Talj

Charara

2018

2018 IEEE International Conference on Systems, Man, and Cybernetics (SMC)

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“…Object detection is the most fundamental task in the computer vision community and has attracted researchers’ attention in different fields. Object detection is not only widely used in video surveillance [ 1 ] and self-driving [ 2 ] but also forms a key component of many other visual tasks, such as scene understanding [ 3 , 4 ] and image guidance [ 5 ]. In recent years, the rapid development of deep Convolutional Neural Networks (CNNs) and well-annotated datasets [ 6 , 7 ] has resulted in many breakthroughs by the computer community.…”

Section: Introductionmentioning

confidence: 99%

Lightweight Feature Enhancement Network for Single-Shot Object Detection

Jia

Liu

2021

Sensors

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At present, the one-stage detector based on the lightweight model can achieve real-time speed, but the detection performance is challenging. To enhance the discriminability and robustness of the model extraction features and improve the detector’s detection performance for small objects, we propose two modules in this work. First, we propose a receptive field enhancement method, referred to as adaptive receptive field fusion (ARFF). It enhances the model’s feature representation ability by adaptively learning the fusion weights of different receptive field branches in the receptive field module. Then, we propose an enhanced up-sampling (EU) module to reduce the information loss caused by up-sampling on the feature map. Finally, we assemble ARFF and EU modules on top of YOLO v3 to build a real-time, high-precision and lightweight object detection system referred to as the ARFF-EU network. We achieve a state-of-the-art speed and accuracy trade-off on both the Pascal VOC and MS COCO data sets, reporting 83.6% AP at 37.5 FPS and 42.5% AP at 33.7 FPS, respectively. The experimental results show that our proposed ARFF and EU modules improve the detection performance of the ARFF-EU network and achieve the development of advanced, very deep detectors while maintaining real-time speed.

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“…Because the driver in dangerous driving state, the reaction speed and attention will be reduced, resulting in abnormal movement of vehicles, such as lane deviation. Therefore, many scholars have established a way to identify the driver's dangerous driving conditions by means of vehicle speed variation, lateral deviation, acceleration, and deceleration [13][14][15]. The United States AssistWare Technology company developed the SafeTrace system; the system uses the front camera to detect the front lane line, according to the deviation of the vehicle to identify the driver's dangerous driving status.…”

Section: Introductionmentioning

confidence: 99%

Extraction Method of Driver’s Mental Component Based on Empirical Mode Decomposition and Approximate Entropy Statistic Characteristic in Vehicle Running State

Zhao

Guo

Zhang

2017

Journal of Advanced Transportation

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In the driver fatigue monitoring technology, the essence is to capture and analyze the driver behavior information, such as eyes, face, heart, and EEG activity during driving. However, ECG and EEG monitoring are limited by the installation electrodes and are not commercially available. The most common fatigue detection method is the analysis of driver behavior, that is, to determine whether the driver is tired by recording and analyzing the behavior characteristics of steering wheel and brake. The driver usually adjusts his or her actions based on the observed road conditions. Obviously the road path information is directly contained in the vehicle driving state; if you want to judge the driver’s driving behavior by vehicle driving status information, the first task is to remove the road information from the vehicle driving state data. Therefore, this paper proposes an effective intrinsic mode function selection method for the approximate entropy of empirical mode decomposition considering the characteristics of the frequency distribution of road and vehicle information and the unsteady and nonlinear characteristics of the driver closed-loop driving system in vehicle driving state data. The objective is to extract the effective component of the driving behavior information and to weaken the road information component. Finally the effectiveness of the proposed method is verified by simulating driving experiments.

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Overtaking & receding vehicle detection for driver assistance and naturalistic driving studies

Cited by 13 publications

References 20 publications

Cooperative Lateral Maneuvers Manager for Multi-autonomous Vehicles

Cooperative Lateral Maneuvers Manager for Multi-autonomous Vehicles

Lightweight Feature Enhancement Network for Single-Shot Object Detection

Extraction Method of Driver’s Mental Component Based on Empirical Mode Decomposition and Approximate Entropy Statistic Characteristic in Vehicle Running State

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