Research on risky driving behavior evaluation model based on CIDAS real data

Hu, Lin; Bao, Xingqian; Ma, Lin; Yu, Chao; Fang, Wenchang

doi:10.1177/0954407020985972

Cited by 43 publications

(24 citation statements)

References 28 publications

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“…TBIs not only bring immeasurable pain to patients but also cause huge losses to the whole society. The main causes of TBI are traffic accidents, falls, and attacks, with traffic accidents being the second largest cause of TBI ( Gabler et al, 2016 ; Hu et al, 2020 , 2021 ). Therefore, studies on TBIs in traffic accidents have great practical significance.…”

Section: Introductionmentioning

confidence: 99%

Evaluation of Head Injury Criteria for Injury Prediction Effectiveness: Computational Reconstruction of Real-World Vulnerable Road User Impact Accidents

Wang

et al. 2021

Front. Bioeng. Biotechnol.

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This study evaluates the effectiveness of various widely used head injury criteria (HICs) in predicting vulnerable road user (VRU) head injuries due to road traffic accidents. Thirty-one real-world car-to-VRU impact accident cases with detailed head injury records were collected and replicated through the computational biomechanics method; head injuries observed in the analyzed accidents were reconstructed by using a finite element (FE)-multibody (MB) coupled pedestrian model [including the Total Human Model for Safety (THUMS) head–neck FE model and the remaining body segments of TNO MB pedestrian model], which was developed and validated in our previous study. Various typical HICs were used to predict head injuries in all accident cases. Pearson’s correlation coefficient analysis method was adopted to investigate the correlation between head kinematics-based injury criteria and the actual head injury of VRU; the effectiveness of brain deformation-based injury criteria in predicting typical brain injuries [such as diffuse axonal injury diffuse axonal injury (DAI) and contusion] was assessed by using head injury risk curves reported in the literature. Results showed that for head kinematics-based injury criteria, the most widely used HICs and head impact power (HIP) can accurately and effectively predict head injury, whereas for brain deformation-based injury criteria, the maximum principal strain (MPS) behaves better than cumulative strain damage measure (CSDM0.15 and CSDM0.25) in predicting the possibility of DAI. In comparison with the dilatation damage measure (DDM), MPS seems to better predict the risk of brain contusion.

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Section: Introductionmentioning

confidence: 99%

Evaluation of Head Injury Criteria for Injury Prediction Effectiveness: Computational Reconstruction of Real-World Vulnerable Road User Impact Accidents

Wang

et al. 2021

Front. Bioeng. Biotechnol.

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“…Nowadays, with the rapid developments of the economy and industrial automation, the shortage of resources (such as non-renewable energy, oil [53], and coal [54]) and environmental pollution (air pollution [55] and human health [56]) are becoming more and more serious [57]. How to effectively control energy shortage [58][59][60][61][62][63] and engine emission reduction [64][65][66][67][68][69][70][71][72][73][74] are the main areas of interest for researchers today. In this paper, an improved model was developed using AVL-BOOST software and employed to investigate the effects of methanol-diesel with different methanol ratios (DM0, DM10, DM20, DM30, and DM40) on engine combustion and emission characteristics at different speeds.…”

Section: Discussionmentioning

confidence: 99%

Effects of Different Mixture Ratios of Methanol-Diesel on the Performance Enhancement and Emission Reduction for a Diesel Engine

Zhang

Tian

et al. 2021

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To improve the combustion and emission characteristics of diesel engines, methanol-diesel fuels with different mixing ratios (DM0, DM10, DM20, DM30, and DM40) were used to investigate the effects of methanol addition on the combustion and emission of a four-stroke diesel engine in terms of cylinder pressure, brake power, brake-specific fuel consumption, and nitrogen oxides, soot, and carbon monoxide emissions. Firstly, an improved entire diesel engine model was developed using AVL-BOOST software and validated by the experimental results. The results showed that the increase of methanol content in the fuel mixture had a negative impact on the performance characteristic of the diesel engine, but significantly improved the emission characteristic of the diesel engine. With the methanol ratio in the mixed fuel increased to 10%, 20%, 30%, and 40%, the cylinder pressure of the engine increased by 0.89%, 1.48%, 2.29%, and 3.17%, respectively. However, the power decreased by 3.76%, 6.74%, 11.35%, and 15.45%, the torque decreased by 3.76%, 6.74%, 11.35%, and 15.45%, respectively, and the brake specific fuel consumption increased by 3.77%, 6.92%, 12.33%, and 17.61%, respectively. In addition, with the methanol ratio in the mixed fuel increased to 10%, 20%, 30%, and 40%, the carbon monoxide emission decreased by 21.32%, 39.04%, 49.81%, and 56.59% and the soot emission decreased by 0.25%, 8.69%, 16.81%, and 25.28%, respectively. Therefore, the addition of methanol to the fuel can improve the combustion and emission characteristics of the engine.

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“…As these models make driver judgments based on historical vehicle trajectory, without the influence of the other vehicles entering the intersection, the collected data are imperfect, and the judgment results have errors. Therefore, future research can use onboard networking equipment, sensors, and CAN or CIDAS data [43] to improve judgment accuracy.…”

Section: Prediction Of Iov Driving Behavior On Roadsmentioning

confidence: 99%

“…The introduction of HMM, driver intention analysis, and other technologies can establish new research directions, enrich the research on autonomous driving at intersections, and improve the safety and comfort of driving behavior prediction in intermittent traffic flow.As these models make driver judgments based on historical vehicle trajectory, without the influence of the other vehicles entering the intersection, the collected data are imperfect, and the judgment results have errors. Therefore, future research can use onboard networking equipment, sensors, and CAN or CIDAS data[43] to improve judgment accuracy.…”

mentioning

confidence: 99%

Research Progress and Prospects of Vehicle Driving Behavior Prediction

Zheng

2021

WEVJ

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Autonomous driving technology is vital for intelligent transportation systems. Vehicle driving behavior prediction is the foundation and core of autonomous driving. A detailed review of the existing research on vehicle driving behavior prediction can improve the understanding of the current progress of research on autonomous driving and provide references for follow-up researchers. This paper primarily reviews and analyzes the control models of autonomous driving, prejudgment methods, on-road and intersection traffic decision-making, and shortcomings of the research about the prediction of individual intelligent vehicle driving behavior, the prediction on movements of vehicles connected via the Internet, and prediction of driving behavior in a mixed traffic environment. The deficiencies in the research on vehicle driving behavior prediction are as follows: (1) there are numerous limitations in the intelligent application scenarios of individual intelligent vehicles; (2) although the Internet of Vehicles is a significant developmental trend, the training and test datasets are not rich enough; and (3) as the research of mixed traffic flow is still in the initial stages, the comfort brought by autonomous driving in hybrid driving environments is not being considered. In addition to the above analyses and comments, the future research prospects of vehicle driving behavior prediction are discussed as well.

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Research on risky driving behavior evaluation model based on CIDAS real data

Cited by 43 publications

References 28 publications

Evaluation of Head Injury Criteria for Injury Prediction Effectiveness: Computational Reconstruction of Real-World Vulnerable Road User Impact Accidents

Evaluation of Head Injury Criteria for Injury Prediction Effectiveness: Computational Reconstruction of Real-World Vulnerable Road User Impact Accidents

Effects of Different Mixture Ratios of Methanol-Diesel on the Performance Enhancement and Emission Reduction for a Diesel Engine

Research Progress and Prospects of Vehicle Driving Behavior Prediction

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