Study on the Driving Style Adaptive Vehicle Longitudinal Control Strategy

This paper presents a fusion control strategy of adaptive cruise control(ACC) and collision avoidance(CA),which takes into account a driver’s behavioral style. First, a questionnaire survey was performed to identify driver type, and the corresponding driving behavioral data were collected via driving simulator experiments, which served as the template data for the online identification of driver type. Then, the driveradaptive ACC/CA fusion control strategy was designed, and its effect was verified by virtual experiments. The results indicate that the proposed control strategy could achieve the fusion control of ACC and CA successfully and improve driver adaptability and comfort.

Driving Style Recognition System Using Smartphone Sensors Based on Fuzzy Logic

Every 24 seconds,someone dies on the road due to road accidents and it is the 8th leading cause of death and the first among children aged 15–29 years.1.35 million people globally die every year due to road traffic crashes.An additional 20–50 million suffer from non-fatal injuries,often resulting in longterm disabilities.This costs around 3%of Gross Domestic Product to most countries,and it is a considerable economic loss.The governments have taken various measures such as better road infrastructures and strict enforcement of motor-vehicle laws to reduce these accidents.However,there is still no remarkable reduction in the number of accidents.To ensure driver safety and achieve vision of zero accidents,there is a great need to monitor drivers’driving styles.Most of the existing driving behavior monitoring solutions are based on expensive hardware sensors.As most people are using smartphones in the modern era,a system based on mobile application is proposed,which can reduce the cost for developing intelligent transport systems(ITS)to a large extent.In this paper,we utilize the accelerometer sensor data and the global positioning system(GPS)sensor deployed in smartphones to recognize driving and speeding events.A driving style recognition system based on fuzzy logic is designed to classify different driving styles and control reckless driving by taking the longitudinal/lateral acceleration and speed as input parameters.Thus,the proposed system uses fuzzy logic rather than taking the crisp values of the sensors.Results indicate that the proposed system can classify reckless driving based on fuzzy logic and,therefore,reduce the number of accidents.

A dynamic speed guidance method at on-ramp merging areas of urban expressway considering driving styles

Dynamic speed guidance for vehicles in on-ramp merging zones is instrumental in alleviating traffic congestion on urban expressways.To enhance compliance with recommended speeds,the development of a dynamic speed-guidance mechanism that accounts for heterogeneity in human driving styles is pivotal.Utilizing intelligent connected technologies that provide real-time vehicular data in these merging locales,this study proposes such a guidance system.Initially,we integrate a multi-agent consensus algorithm into a multi-vehicle framework operating on both the mainline and the ramp,thereby facilitating harmonized speed and spacing strategies.Subsequently,we conduct an analysis of the behavioral traits inherent to drivers of varied styles to refine speed planning in a more efficient and reliable manner.Lastly,we investigate a closed-loop feedback approach for speed guidance that incorporates the driver’s execution rate,thereby enabling dynamic recalibration of advised speeds and ensuring fluid vehicular integration into the mainline.Empirical results substantiate that a dynamic speed guidance system incorporating driving styles offers effective support for human drivers in seamless mainline merging.

A Coaching Program for Recently Licensed Young Drivers in the Netherlands： Which Drivers Are Attracted？

In line with European developments, a Dutch second phase coaching program, referred to as the DX- （Driver Xperience） program, was developed for young novice drivers to counteract their high accident risk. More specifically, the aim of the DX-program was to enable young drivers to make responsible decisions and develop positive attitudes regarding four levels of the driving task： combining life style and driving, planning and navigation, participating in different traffic situations and handling the vehicle. In this paper, the design principles of the program are described. The empirical study focused on the entry characteristics of the participating young drivers （n = 3,117） as compared to a reference group of young drivers （n = 345）. Results show that the DX-program attracted young drivers that, in some respects, showed a more risky profile than average young drivers in terms of speed violations, anger and the number of fines. In addition, four groups of participants with sharply differing driving styles could be distinguished. Implications for educational design and follow-up research are discussed within the theoretical framework of self-regulated learning.

Safety Evaluation of Commercial Vehicle Driving Behavior Using the AHP–CRITIC Algorithm

To prevent and reduce road traffic accidents and improve driver safety awareness and bad driving be-haviors,we propose a safety evaluation method for commercial vehicle driving behavior.Three driving style clas-sification indexes were extracted using driving data from commercial vehicles and four primary and ten secondary safety evaluation indicators.Based on the stability of commercial vehicles transporting goods,the acceleration index is divided into three levels according to the statistical third quartile,and the evaluation expression of the safety index evaluation is established.Drivers were divided into conservative,moderate,and radical using K-means++.The weights corresponding to each index were calculated using a combination of the analytic hierarchy process(AHP)and criteria importance through intercriteria correlation(CRITIC),and the driving behavior scores of various drivers were calculated according to the safety index score standard.The established AHP-CRITIC safety evaluation model was verified using the actual driving behavior data of commercial vehicle drivers.The calculation results show that the proposed evaluation model can clearly distinguish between the types of drivers with different driving styles,verifying its rationality and validity.The evaluation results can provide a reference for transportation management departments and enterprises.

Real-time comprehensive driving ability evaluation algorithm for intelligent assisted driving

To meet the needs of the human-machine co-driving decision problem in the intelligent assisted driving system for real-time comprehensive driving ability evaluation of drivers,this paper proposes a real-time comprehensive driving ability evaluation method that integrates driving skill,driving state,and driving style.Firstly,by analyzing the driving experiment data obtained based on the intelligent driving simulation platform(the experiment can effectively distinguish the driver's driving skills and avoid the interference of driving style),the feature values that significantly represent driving skills and driving state are selected,and the time correlation between driving state and driving skills is pointed out.Furthermore,the concept of relativity in comprehensive driving ability evaluation is further proposed.Under this concept,the natural driving trajectory dataset-HighD is used to establish the distribution map of feature values of the human driver group as the evaluation benchmark to realize the relative evaluation of driving skill and driving state.Similarly,HighD is used to establish a distribution map of human driver style feature values as an evaluation benchmark to achieve relative driving style evaluation.Finally,a comprehensive driving ability evaluation model with a“punishment”and“affirmation”mechanism is proposed.The experimental comparative analysis shows that the evaluation algorithm proposed in this paper can take into account the driver's driving skill,driving state,and driving style in the real-time comprehensive driving ability evaluation,and draw differential evaluation conclusions based on the“punishment”and“affirmation”mechanism model to achieve a comprehensive and objective evaluation of the driver's driving ability.It can meet the needs of human-machine shared driving decisions for driver's driving ability evaluation.

Effect of gender and personality characteristics on the speed tendency based on advanced driving assistance system(ADAS)evaluation

Purpose–This study aims to explore the relationship between speed behavior of participants and driving styles on interchange ramps.A spiral interchange in Chongqing was selected as an experimental road to carry outfield driving experiment.Design/methodology/approach–The continuous operating speed during experiment was selected by Mobile Eye,and the driving style was selected via two inventories.Findings–Different driving behaviors showed great differences in age,driving mileage and driving experience.During driving process,male pursued driving stimulation more,whereas female pursued driving steadiness more.Therefore,driving characteristics of male were more disadvantageous to driving safety than that of female.Except for the large speed difference at the entrance and exit of the ramps,the differences at other positions were small.And the operating speed of male was slightly higher than that of female.The difference between different genders at the ascending end position achieved 4–5 kph,and the difference at other feature points were mostly 1–2 kph.During driving process,risky participants were more likely to pursue driving stimulation,and the poor speed control behavior was reflected in wide range of desired operating speed.Based on the results of analyzing at feature points,melancholy and sanguine participants more tended to take a high operating speed,and the poor speed control behavior was reflected in the most widely desired speed range.The speed control behavior of mixed participants was more cautious.Originality/value–Advanced driving assistance system combined with two inventories was used to explore difference of speed behavior.

Potential impact of autonomous vehicles in mixed traffic from simulation using real traffic flow

This work focuses on the potential impacts of the autonomous vehicles in a mixed traffic condition represented in traffic simulator Simulation of Urban MObility(SUMO)with real traffic flow.Specifically,real traffic flow and speed data collected in 2002 and 2019 in Gothenburg were used to simulate daily flow variation in SUMO.In order to predict the most likely drawbacks during the transition from a traffic consisting only manually driven vehicles to a traffic consisting only fully-autonomous vehicles,this study focuses on mixed traffic with different percentages of autonomous and manually driven vehicles.To realize this aim,several parameters of the car following and lane change models of autonomous vehicles are investigated in this paper.Along with the fundamental diagram,the number of lane changes and the number of conflicts are analyzed and studied as measures for improving road safety and efficiency.The study highlights that the autonomous vehicles’features that improve safety and efficiency in 100%autonomous and mixed traffic are different,and the ability of autonomous vehicles to switch between mixed and autonomous driving styles,and vice versa depending on the scenario,is necessary.