Influence of traffic congestion on driver behavior in post-congestion driving

Li, Guofa; Lai, Weijian; Sui, Xiaoxuan; Li, Xiaohang; Qu, Xingda; Zhang, Tingru; Li, Yuezhi

doi:10.1016/j.aap.2020.105508

Cited by 134 publications

(70 citation statements)

References 67 publications

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“…The study of driver behavior can be of great help in the development of RADAR's determination of detection distance. [16] (Fig. 23) Another challenge is for the vehicle to respond only to objects that cross the planned route.…”

Section: Remark Of the Situationmentioning

confidence: 99%

Conflicts of Automated Driving With Conventional Traffic Infrastructure

2020

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Road traffic signals and traffic infrastructure have a significant impact on the behaviour of highly automated or autonomous vehicles. However, the increase in automation does not always mean an advantage. Generally, highly automated vehicles strictly follow the traffic rules resulting in near-accident situations, although their goal is to avoid and reduce them. Malfunctions of the automated functions might cause surprising interventions while the vehicle is in motion, drivers cannot react in time and well. This paper highlights the potential danger and uncertainty of highly automated or autonomous vehicles in the context of the current conventional traffic infrastructure system. In the future, special consideration shall be given to the vehicle industry and traffic regulation makers on how the infrastructure should be adapted to automated vehicle functions to have a seamless shift towards automated driving. The paper sums up many problematic situations with two critical problems with sensitivity analysis. The first situation: the speed assist system (based on speed limit sign recognition) conflicts with the traffic infrastructure. The second situation is shown: the ACC (Adaptive Cruise Control) and LKA (Lane Assist) contradicts with the traffic infrastructure. These critical situations were investigated by using an high-fidelity automotive simulation software as proof of concept and were examined by accident reconstruction analysis software. INDEX TERMS critical traffic situations, highly automated vehicles, autonomous driving, traffic infrastructure

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Section: Remark Of the Situationmentioning

confidence: 99%

Conflicts of Automated Driving With Conventional Traffic Infrastructure

2020

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“…The best rank-1 (94.8%) and mAP (86.4%) on Market-1501 are simultaneously achieved when c=0. 4 We further examined the standard deviation (Std) of other labels probabilities at each epoch when using the original Softmax loss, label smooth, and our developed ADCSLL. The results are illustrated in Fig.…”

Section: B Performance Of Different Loss Functionsmentioning

confidence: 99%

“…Person re-identification (Re-ID) aims to retrieve a given person from non-overlapping cameras, which is widely applied in the field of intelligent security, intelligent retail and intelligent video surveillance [1]. However, the intraclass variations, caused by large variations in poses, viewpoints, illuminations, background environments and occlusion, are challenging this task [2]- [4]. Moreover, when the number of retrieving persons increases, the interclass similarities also make the task more complicated [5].…”

Section: Introductionmentioning

confidence: 99%

Person Re-Identification Using Additive Distance Constraint With Similar Labels Loss

Lei

Tang³

et al. 2020

IEEE Access

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Despite the promising progress made in recent years, person re-identification (Re-ID) remains a challenging task due to the intra-class variations. Most of the current studies used the traditional Softmax loss for solutions, but its discriminative capability encounters a bottleneck. Therefore, how to improve person Re-ID performance is still a challenging task. To address this problem, we proposed a novel loss function, namely additive distance constraint with similar labels loss (ADCSLL). Specifically, we reformulated the Softmax loss by adding a distance constraint to the ground truth label, based on which similar labels were introduced to enhance the learned features to be much more stable and centralized. Experimental evaluations were conducted on two popular datasets (Market-1501 and DukeMTMC-reID) to examine the effectiveness of our proposed method. The results showed that our proposed ADCSLL was more discriminative than most of the other compared state-of-the-art methods. The rank-1 accuracy and the mAP on Market-1501 were 95.0% and 87.0%, respectively. The numbers were 88.6% and 77.2% on DukeMTMC-reID, respectively.

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“…Forward collision interacts more frequently with lane departure than with other abnormal vehicle states. is is possibly the result of drivers becoming more aggressive and focused on the area ahead after congestion [28]. It can easily cause the vehicle to trigger forward collision warning, and the driver's corresponding emergency actions often cause the vehicle to drift sideways.…”

Section: Relationships Between Abnormal Vehicle Statesmentioning

confidence: 99%

The Relationship between Bus Drivers’ Improper Driving Behaviors and Abnormal Vehicle States Based on Advanced Driver Assistance Systems in Naturalistic Driving

Wang

Chen

Yan

et al. 2020

Mathematical Problems in Engineering

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In order to improve the adaptation of driver to the advanced driver assistance system (ADAS) and optimize the active safety control technology of vehicle under man-computer cooperative driving, this paper investigated the correlation between driver’s improper driving behaviors and abnormal vehicle states under the ADAS. Based on the warning data collected from the driver’s assistance warning system equipped on buses, the interaction between improper behaviors, between abnormal vehicle states, and between improper behaviors and abnormal vehicle states were quantitatively analyzed through the hierarchical clustering method and improved Apriori algorithm. The results showed that eye closure and yawn were high in concurrency (probability: 0.888) and interaction (average probability: 0.946); the interaction among lane departure, rapid acceleration, and rapid deceleration are frequent (average probability: 0.7224); eye closure (average probability: 0.452) and yawn (average probability: 0.444) are likely to induce abnormal vehicle states such as rapid acceleration and rapid deceleration. Some suggestions proposed based on the results are as follows. First, it is suggested that the ADAS should combine the warning modes of eye closure and yawn; second, when the driver closes eyes or yawns, the control of the ADAS over the lateral and longitudinal performance of vehicle should be enhanced; third, the extent of control by the ADAS should be determined according to the relationship probability; finally, the lateral control over the vehicle by the ADAS should be strengthened when there is a forward collision warning.

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Influence of traffic congestion on driver behavior in post-congestion driving

Cited by 134 publications

References 67 publications

Conflicts of Automated Driving With Conventional Traffic Infrastructure

Conflicts of Automated Driving With Conventional Traffic Infrastructure

Person Re-Identification Using Additive Distance Constraint With Similar Labels Loss

The Relationship between Bus Drivers’ Improper Driving Behaviors and Abnormal Vehicle States Based on Advanced Driver Assistance Systems in Naturalistic Driving

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