scholarly journals DrivingSense: Dangerous Driving Behavior Identification Based on Smartphone Autocalibration

2017 ◽  
Vol 2017 ◽  
pp. 1-15 ◽  
Author(s):  
Chunmei Ma ◽  
Xili Dai ◽  
Jinqi Zhu ◽  
Nianbo Liu ◽  
Huazhi Sun ◽  
...  
Keyword(s):  
Estimation Error ◽  
Drunk Driving ◽  
Speed Control ◽  
Driving Behavior ◽  
Sensor Reading ◽  
Driving Behaviors ◽  
Direction Change ◽  
Dangerous Driving ◽  
Filter Noise ◽  
The Impact

Since pervasive smartphones own advanced computing capability and are equipped with various sensors, they have been used for dangerous driving behaviors detection, such as drunk driving. However, sensory data gathered by smartphones are noisy, which results in inaccurate driving behaviors estimations. Some existing works try to filter noise from sensor readings, but usually only the outlier data are filtered. The noises caused by hardware of the smartphone cannot be removed from the sensor reading. In this paper, we propose DrivingSense, a reliable dangerous driving behavior identification scheme based on smartphone autocalibration. We first theoretically analyze the impact of the sensor error on the vehicle driving behavior estimation. Then, we propose a smartphone autocalibration algorithm based on sensor noise distribution determination when a vehicle is being driven. DrivingSense leverages the corrected sensor parameters to identify three kinds of dangerous behaviors: speeding, irregular driving direction change, and abnormal speed control. We evaluate the effectiveness of our scheme under realistic environments. The results show that DrivingSense, on average, is able to detect the driving direction change event and abnormal speed control event with 93.95% precision and 90.54% recall, respectively. In addition, the speed estimation error is less than 2.1 m/s, which is an acceptable range.

Effects of Eco-Driving on Driving Performance

2012 ◽  
Vol 178-181 ◽  
pp. 2859-2862 ◽  
Author(s):  
Xiao Yu Yang ◽  
Dan Li ◽  
Peng Jun Zheng
Keyword(s):  
Energy Saving ◽  
Fuel Consumption ◽  
Speed Control ◽  
Driving Behavior ◽  
Driving Performance ◽  
Driving Behaviors

This paper investigates the effects of two driving behaviors on driving performance, the driving with eco-driving support and the general driving. Through observing and analyzing these driving behaviors in a variety of situations, driving performance under conditions of with and without eco-driving was evaluated. Based on the measurements on fuel consumption, speed control and gear use, it was found that eco-driving device can guide drivers to take proper driving behavior, such as in which way to drive and how to drive in order to achieve energy saving. The paper revealed the effects of eco-driving and how to drive efficiently.

scholarly journals A Data Augmentation Approach to Distracted Driving Detection

2020 ◽  
Vol 13 (1) ◽  
pp. 1
Author(s):  
Jing Wang ◽  
ZhongCheng Wu ◽  
Fang Li ◽  
Jun Zhang
Keyword(s):  
Data Augmentation ◽  
Driving Behavior ◽  
Mapping Method ◽  
Distracted Driving ◽  
Detection Model ◽  
Driving Behaviors ◽  
Actual Application ◽  
Original Dataset ◽  
Dangerous Driving ◽  
Activation Mapping

Distracted driving behavior has become a leading cause of vehicle crashes. This paper proposes a data augmentation method for distracted driving detection based on the driving operation area. First, the class activation mapping method is used to show the key feature areas of driving behavior analysis, and then the driving operation areas are detected by the faster R-CNN detection model for data augmentation. Finally, the convolutional neural network classification mode is implemented and evaluated to detect the original dataset and the driving operation area dataset. The classification result achieves a 96.97% accuracy using the distracted driving dataset. The results show the necessity of driving operation area extraction in the preprocessing stage, which can effectively remove the redundant information in the images to get a higher classification accuracy rate. The method of this research can be used to detect drivers in actual application scenarios to identify dangerous driving behaviors, which helps to give early warning of unsafe driving behaviors and avoid accidents.

scholarly journals The Impact of Aggressive Driving Behavior on Driver-Injury Severity at Highway-Rail Grade Crossings Accidents

2018 ◽  
Vol 2018 ◽  
pp. 1-10 ◽  
Author(s):  
Changxi Ma ◽  
Wei Hao ◽  
Wang Xiang ◽  
Wei Yan
Keyword(s):  
Open Space ◽  
Injury Severity ◽  
Mixed Logit ◽  
Weather Conditions ◽  
Driving Behavior ◽  
Aggressive Driving ◽  
Driving Behaviors ◽  
Grade Crossings ◽  
High Level ◽  
The Impact

The effect of aggressive driving behavior on driver’s injury severity is analyzed by considering a comprehensive set of variables at highway-rail grade crossings in the US. In doing so, we are able to use a mixed logit modelling approach; the study explores the determinants of driver-injury severity with and without aggressive driving behaviors at highway-rail grade crossings. Significant differences exist between drivers’ injury severity with and without aggressive driving behaviors at highway-rail grade crossings. The level of injury for younger male drivers increases a lot if they are with aggressive driving behavior. In addition, driving during peak-hour is found to be a statistically significant predictor of high level injury severity with aggressive driving behavior. Moreover, environmental factors are also found to be statistically significant. The increased level of injury severity accidents happened for drivers with aggressive driving behavior in the morning peak (6-9 am), and the probability of fatality increases in both snow and fog condition. Driving in open space area is also found to be a significant factor of high level injury severity with aggressive driving behaviors. Bad weather conditions are found to increase the probability of drivers’ high level injury severity for drivers with aggressive driving behaviors.

Study on Changes of Vehicle Driving Behavior Characteristics for Alcohol Used Drivers

2014 ◽  
Vol 1030-1032 ◽  
pp. 2115-2120
Author(s):  
Ming En Zhong ◽  
Han Chi Hong ◽  
Jian Jin Cai
Keyword(s):  
Drunk Driving ◽  
Driving Behavior ◽  
Transportation Safety ◽  
Simulated Driving ◽  
Driving Behaviors ◽  
Obvious Change ◽  
Driving Speed ◽  
Frequency Probability ◽  
Behavior Characteristics

Drunk driving is proved to be dangerous for traffic transportation safety. However, there are still no specific conclusions about what changes do drinking make to each kind of driving behaviors. This paper set out a drunk states induce program and a simulated driving experiment to measure the data about driving speed, overspeed driving probability, brake frequency, probability of deceleration for avoidance and probability of running red lights when the drivers were in different degree of drunk states. Results showed that driving speed increases significantly only in state of heavy drunk while decreases slightly in state of moderate drunk. Overspeed probability grows higher as the drivers drink more. Brake frequency increases slightly in state of light drunk, but has no obvious change in state of moderate drunk, however decreases significantly in state of heavy drunk. Probability of deceleration for avoidance decreases a little in state of light drunk but significantly in state of heavy drunk, however increases in state of moderate dunk. Probability of running red lights increases significantly only in state of heavy drunk, but has no obvious change in both states of light drunk and moderate drunk. All these support a conclusion that drinking has different influences on each kind of driving behaviors, which perform differently for traffic transportation safety. Judgments for drunk driving related issues should be decided on each specific matter.

Autonomous Vehicles’ Car-Following Drivability Evaluation Based on Driving Behavior Spectrum Reference Model

2021 ◽  
pp. 036119812199485
Author(s):  
Xiao Qi ◽  
Ying Ni ◽  
Yiming Xu ◽  
Ye Tian ◽  
Junhua Wang ◽  
...  
Keyword(s):  
Autonomous Vehicles ◽  
Reference Model ◽  
Numerical Test ◽  
Safety Evaluation ◽  
Driving Behavior ◽  
Car Following ◽  
Driving Behaviors ◽  
Human Driver ◽  
Normal Human ◽  
Naturalistic Driving

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.

Lateral obstacle avoidance control based on driving behavior recognition of the preceding vehicles in adjacent lanes

2020 ◽  
Vol 68 (10) ◽  
pp. 880-892
Author(s):  
Youguo He ◽  
Xing Gong ◽  
Chaochun Yuan ◽  
Jie Shen ◽  
Yingkui Du
Keyword(s):  
Obstacle Avoidance ◽  
Lateral Displacement ◽  
Longitudinal Velocity ◽  
Driving Behavior ◽  
Lateral Acceleration ◽  
Lateral Motion ◽  
Behavior Recognition ◽  
Dynamic Surface ◽  
Lane Changing ◽  
The Impact

AbstractThis paper proposes a lateral lane change obstacle avoidance constraint control simulation algorithm based on the driving behavior recognition of the preceding vehicles in adjacent lanes. Firstly, the driving behavior of the preceding vehicles is recognized based on the Hidden Markov Model, this research uses longitudinal velocity, lateral displacement and lateral velocity as the optimal observation signals to recognize the driving behaviors including lane-keeping, left-lane-changing or right-lane-changing; Secondly, through the simulation of the dangerous cutting-in behavior of the preceding vehicles in adjacent lanes, this paper calculates the ideal front wheel steering angle according to the designed lateral acceleration in the process of obstacle avoidance, designs the vehicle lateral motion controller by combining the backstepping and Dynamic Surface Control, and the safety boundary of the lateral motion is constrained based on the Barrier Lyapunov Function; Finally, simulation model is built, and the simulation results show that the designed controller has good performance. This active safety technology effectively reduces the impact on the autonomous vehicle safety when the preceding vehicle suddenly cuts into the lane.

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

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.

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

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.

Evaluation of the daily cycle in the simulation of the ClimEx large-ensemble

2021 ◽  
Author(s):  
Anne-Marie Begin
Keyword(s):  
Climate Change ◽  
Estimation Error ◽  
Natural Variability ◽  
Extreme Weather Events ◽  
Large Ensemble ◽  
Daily Cycle ◽  
Minimum Number ◽  
The Mean ◽  
Mean Cycle ◽  
The Impact

<p>To estimate the impact of climate change on our society we need to use climate projections based on numerical models. These models make it possible to assess the effects on climate of the increase in greenhouse gases (GHG) as well as natural variability. We know that the global average temperature will increase and that the occurrence, intensity and spatio-temporal distribution of extreme precipitations will change. These extreme weather events cause droughts, floods and other natural disasters that have significant consequences on our life and environment. Precipitation is a key variable in adapting to climate change.</p><p> </p><p>This study focuses on the ClimEx large ensemble, a set of 50 independent simulations created to study the effect of climate change and natural variability on the water network in Quebec. This dataset consists of simulations produced using the Canadian Regional Climate Model version 5 (CRCM5) at 12 km of resolution driven by simulations from the second generation Canadian Earth System Model (CanESM2) global model at 310 km of resolution.</p><p> </p><p>The aim of the project is to evaluate the performance of the ClimEx ensemble in simulating the daily cycle and representing extreme values.  To get there, 30 years of hourly time series for precipitation and 3 hourly for temperature are analyzed. The simulations are compared with the values from the simulation of CRCM5 driven by ERA-Interim reanalysis, the ERA5 reanalysis and Environment and Climate Change Canada (ECCC) stations. An evaluation of the sensitivity of different statistics to the number of members is also performed.</p><p> </p><p>The daily cycle of precipitation from ClimEx shows mainly non-significant correlations with the other datasets and its amplitude is less than the observation datas from ECCC stations. For temperature, the correlation is strong and the amplitude of the cycle is similar to observations. ClimEx provides a fairly good representation of the 95, 97, 99<sup>th</sup> quantiles for precipitation. For temperature it represents a good distribution of quantiles but with a warm bias in southern Quebec. For precipitation hourly maximum, ClimEx shows values 10 times higher than ERA5.  For temperature, minimum and maximum values may exceed the ERA5 limit by up to 20°C. For precipitation, the minimum number of members for the estimation of the 95 and 99<sup>th</sup><sup></sup>quantiles and the mean cycle is between 15 and 50 for an estimation error of less than 5%. For the 95, 99<sup>th</sup> quantiles of temperature, the minimum number of members is between 1 and 17 and for the mean cycle 1 to 2 members are necessary to obtain an estimation error of less than 0.5°C.</p>

Parameter Sensitivity Analysis of a Cooperative Dynamic Bus Lane System With Connected Vehicles

2021 ◽  
pp. 036119812110357
Author(s):  
Meng Xie ◽  
Michael Winsor ◽  
Tao Ma ◽  
Andreas Rau ◽  
Fritz Busch ◽  
...  
Keyword(s):  
Travel Time ◽  
Transition Period ◽  
Penetration Rate ◽  
Simulation Models ◽  
Driving Behavior ◽  
Connected Vehicles ◽  
Microscopic Simulation ◽  
Communication Range ◽  
Bus Lane ◽  
The Impact

This paper aims to evaluate the sensitivity of the proposed cooperative dynamic bus lane system with microscopic traffic simulation models. The system creates a flexible bus priority lane that is only activated on demand at an appropriate time with advanced information and communication technologies, which can maximize the use of road space. A decentralized multi-lane cooperative algorithm is developed and implemented in a microscopic simulation environment to coordinate lane changing, gap acceptance, and car-following driving behavior for the connected vehicles (CVs) on the bus lane and the adjacent lanes. The key parameters for the sensitivity study include the penetration rate and communication range of CVs, considering the transition period and gradual uptake of CVs. Multiple scenarios are developed and compared to analyze the impact of key parameters on the system’s performance, such as total saved travel time of all passengers and travel time variation among buses and private vehicles. The microscopic simulation models showed that the cooperative dynamic bus lane system is significantly sensitive to the variations of the penetration rate and the communication range in a congested traffic state. With a CV system and a communication range of 150 m, buses obtain maximum benefits with minimal impacts on private vehicles in the study simulation. The safety concerns induced by cooperative driving behavior are also discussed in this paper.

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