The Effects of Lisdexamfetamine Dimesylate on Driving Behaviors in Young Adults With ADHD Assessed With the Manchester Driving Behavior Questionnaire

A large portion of the accidents involving autonomous vehicles (AVs) are not caused by the functionality of AV, but rather because of human intervention, since AVs’ driving behavior was not properly understood by human drivers. Such misunderstanding leads to dangerous situations during interaction between AV and human-driven vehicle (HV). However, few researches considered HV-AV interaction safety in AV safety evaluation processes. One of the solutions is to let AV mimic a normal HV’s driving behavior so as to avoid misunderstanding to the most extent. Therefore, to evaluate the differences of driving behaviors between existing AV and HV is necessary. DRIVABILITY is defined in this study to characterize the similarity between AV’s driving behaviors and expected behaviors by human drivers. A driving behavior spectrum reference model built based on human drivers’ behaviors is proposed to evaluate AVs’ car-following drivability. The indicator of the desired reaction time (DRT) is proposed to characterize the car-following drivability. Relative entropy between the DRT distribution of AV and that of the entire human driver population are used to quantify the differences between driving behaviors. A human driver behavior spectrum was configured based on naturalistic driving data by human drivers collected in Shanghai, China. It is observed in the numerical test that amongst all three types of preset AVs in the well-received simulation package VTD, the brisk AV emulates a normal human driver to the most extent (ranking at 55th percentile), while the default AV and the comfortable AV rank at 35th and 8th percentile, respectively.

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Exploring the Influence of E-Hailing Applications on the Taxi Industry—From the Perspective of the Drivers

ISPRS International Journal of Geo-Information ◽

10.3390/ijgi10020077 ◽

2021 ◽

Vol 10 (2) ◽

pp. 77

Author(s):

Yitong Gan ◽

Hongchao Fan ◽

Wei Jiao ◽

Mengqi Sun

Keyword(s):

Driving Behavior ◽

Urban Traffic ◽

Behavior Changes ◽

Transportation Industry ◽

Trajectory Data ◽

Long Distance ◽

Taxi Drivers ◽

Driving Behaviors ◽

Taxi Industry ◽

The Times

In China, the traditional taxi industry is conforming to the trend of the times, with taxi drivers working with e-hailing applications. This reform is of great significance, not only for the taxi industry, but also for the transportation industry, cities, and society as a whole. Our goal was to analyze the changes in driving behavior since taxi drivers joined e-hailing platforms. Therefore, this paper mined taxi trajectory data from Shanghai and compared the data of May 2015 with those of May 2017 to represent the before-app stage and the full-use stage, respectively. By extracting two-trip events (i.e., vacant trip and occupied trip) and two-spot events (i.e., pick-up spot and drop-off spot), taxi driving behavior changes were analyzed temporally, spatially, and efficiently. The results reveal that e-hailing applications mine more long-distance rides and new pick-up locations for drivers. Moreover, driver initiative have increased at night since using e-hailing applications. Furthermore, mobile payment facilities save time that would otherwise be taken sorting out change. Although e-hailing apps can help citizens get taxis faster, from the driver’s perspective, the apps do not reduce their cruising time. In general, e-hailing software reduces the unoccupied ratio of taxis and improves the operating ratio. Ultimately, new driving behaviors can increase the driver’s revenue. This work is meaningful for the formulation of reasonable traffic laws and for urban traffic decision-making.

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Examining Risky Driving Behavior Among Older Drivers Using Self-Reported and Objective Data

Innovation in Aging ◽

10.1093/geroni/igaa057.1507 ◽

2020 ◽

Vol 4 (Supplement_1) ◽

pp. 465-465

Author(s):

Jennifer Zakrajsek ◽

Lisa Molnar ◽

David Eby ◽

David LeBlanc ◽

Lidia Kostyniuk ◽

...

Keyword(s):

Motor Vehicle ◽

Seat Belt ◽

Driving Behavior ◽

Video Data ◽

Older Drivers ◽

Driving Frequency ◽

Middle Aged ◽

Risky Driving ◽

Driving Behaviors ◽

Study Results

Abstract Motor vehicle crashes represent a significant public health problem. Efforts to improve driving safety are multifaceted, focusing on vehicles, roadways, and drivers with risky driving behaviors playing integral roles in each area. As part of a study to create guidelines for developing risky driving countermeasures, 480 drivers (118 young/18-25, 183 middle-aged/35-55, 179 older/65 and older) completed online surveys measuring driving history, risky driving (frequency of engaging in distracted [using cell phone, texting, eating/drinking, grooming, reaching/interacting] and reckless/aggressive [speeding, tailgating, failing to yield right-of-way, maneuvering unsafely, rolling stops] driving behaviors), and psychosocial characteristics. A cluster analysis using frequency of the risky behaviors and seat belt use identified five risky behavior-clusters: 1) rarely/never distracted-rarely/never reckless/aggressive (n=392); 2) sometimes distracted-rarely/never reckless/aggressive (n=33); 3) sometimes distracted-sometimes reckless/aggressive (n=40); 4) often/always distracted-often/always reckless/aggressive (n=11); 5) no pattern (n=4). Older drivers were more likely in the first/lowest cluster (93.8% of older versus 84.2% of middle-aged and 59.3% of young drivers; p<.0001). Fifteen older drivers participated in a follow-up study in which their vehicles were equipped with a data acquisition system that collected objective driving and video data of all trips for three weeks. Analysis of video data from 145 older driver trips indicated that older drivers engaged in at least one distracted behavior in 115 (79.3%) trips. While preliminary, this suggests considerably more frequent engagement in distracted driving than self-reported and that older drivers should not be excluded from consideration when developing risky driving behavior countermeasures. Full study results and implications will be presented.

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Personality factors as predictors of persistent risky driving behavior and crash involvement among young adults

Injury Prevention ◽

10.1136/ip.2007.015925 ◽

2007 ◽

Vol 13 (6) ◽

pp. 376-381 ◽

Cited By ~ 35

Author(s):

Pauline Gulliver ◽

Dorothy Begg

Keyword(s):

Young Adults ◽

Driving Behavior ◽

Personality Factors ◽

Risky Driving

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Simulating Dynamic Driving Behavior in Simulation Test for Unmanned Vehicles via Multi-Sensor Data

Sensors ◽

10.3390/s19071670 ◽

2019 ◽

Vol 19 (7) ◽

pp. 1670 ◽

Cited By ~ 1

Author(s):

Danchen Zhao ◽

Yaochen Li ◽

Yuehu Liu

Keyword(s):

Driving Behavior ◽

Unmanned Vehicles ◽

Sensor Data ◽

The Road ◽

Driving Behaviors ◽

Slowing Down ◽

3D Space ◽

Geometric Consistency ◽

Virtual Vehicle ◽

Main Basis

Driving behavior is the main basis for evaluating the performance of an unmanned vehicle. In simulation tests of unmanned vehicles, in order for simulation results to be approximated to the actual results as much as possible, model of driving behaviors must be able to exhibit actual motion of unmanned vehicles. We propose an automatic approach of simulating dynamic driving behaviors of vehicles in traffic scene represented by image sequences. The spatial topological attributes and appearance attributes of virtual vehicles are computed separately according to the constraint of geometric consistency of sparse 3D space organized by image sequence. To achieve this goal, we need to solve three main problems: Registration of vehicle in a 3D space of road environment, vehicle’s image observed from corresponding viewpoint in the road scene, and consistency of the vehicle and the road environment. After the proposed method was embedded in a scene browser, a typical traffic scene including the intersections was chosen for a virtual vehicle to execute the driving tasks of lane change, overtaking, slowing down and stop, right turn, and U-turn. The experimental results show that different driving behaviors of vehicles in typical traffic scene can be exhibited smoothly and realistically. Our method can also be used for generating simulation data of traffic scenes that are difficult to collect.

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Driving Performance Evaluation Correlated to Age and Visual Acuities Based on VR Technologies

Journal of Advanced Transportation ◽

10.1155/2020/5898762 ◽

2020 ◽

Vol 2020 ◽

pp. 1-11

Author(s):

Sooncheon Hwang ◽

Sunhoon Kim ◽

Dongmin Lee

Keyword(s):

Visual Acuity ◽

Driving Simulator ◽

Driving Behavior ◽

Driving Performance ◽

Present System ◽

The South ◽

Dynamic Visual Acuity ◽

South Korean ◽

Driving Behaviors ◽

License System

There is currently much debate regarding the effectiveness of the driver license system in South Korea, due to the numerous traffic crashes caused by drivers who are suspected of having insufficient physical and mental abilities. Through the present system, it is quite difficult to identify such drivers indirectly through physical tests, such as visual acuity tests, since the correlation of such results with driving performance remains unclear. The objective of this study was to investigate the relationship between driving performance and visual acuities for improving the South Korean driver license system. In this study, two investigations were conducted: static and dynamic visual acuity examinations and driving performance tests based on a virtual reality (VR) system. The driving performance was evaluated with a driving simulator, based on driving behaviors in different experimental scenarios, including daytime and nighttime driving on a rural highway, and unexpected incident situations. Here, we produce statistically significant evidence that reduced visual acuity impairs driving performance, and driving behaviors differ significantly among groups with different vision capabilities, especially dynamic vision. Visual acuities, typically dynamic visual acuity, greatly influenced driving behavior, as measured by the standard deviation of speeds and vehicle LPs, and this was especially notable in curved road segments in daytime experiment. These experimental results revealed that the driving performance of participants with impaired dynamic visual acuity was deficient and unsafe. This confirmed that dynamic visual acuity levels are significant determinants of driving behavior, and they well explain driver performance levels. These findings suggest that the South Korean driver license system should include a test of dynamic visual acuity to create better and safer driving.

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