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2016-04-05
Journal Article
2015-01-9152
André Lundkvist, Arne Nykänen, Roger Johnsson
Abstract Many of the information systems in cars require visual attention, and a way to reduce both visual and cognitive workload could be to use sound. An experiment was designed in order to determine how driving and secondary task performance is affected by the use of information sound signals and their spatial positions. The experiment was performed in a driving simulator utilizing Lane Change Task as a driving scenario in combination with the Surrogate Reference Task as a secondary task. Two different signal sounds with different spatial positions informed the driver when a lane change should be made and when a new secondary task was presented. Driving performance was significantly improved when both signal sounds were presented in front of the driver. No significant effects on secondary task performance were found. It is recommended that signal sounds are placed in front of the driver, when possible, if the goal is to draw attention forward.
2016-04-05
Journal Article
2015-01-9153
André Lundkvist, Arne Nykänen
Abstract The number of advanced driver assistance systems is constantly increasing. Many of the systems require visual attention, and a way to reduce risks associated with inattention could be to use multisensory signals. A driver's main attention is in front of the car, but inattention to surrounding areas beside and behind the car can be a risk. Therefore, there is a need for driver assistance systems capable of directing attention to the sides. In a simulator study, combined visual, auditory and vibrotactile signals for directional attention capture were designed for use in driver assistance systems, such as blind spot information, parking assistance, collision warnings, navigation, lane departure warning etc. An experiment was conducted in order to measure the effects of the use of different sensory modalities on directional attention (left/right) in driver assistance systems.
2016-04-05
Technical Paper
2016-01-1422
Tarek Ouali, Nirav Shah, Bill Kim, David Fuente, Bo Gao
This paper introduces a new method for driving style identification based on vehicle communication signals. The purpose of this method is to classify a trip, driven in a vehicle, into three driving style categories: calm, normal or aggressive. The trip is classified based on the vehicle class, the type of road it was driven on (urban, rural or motorway) and different types of driving events (launch, accelerating and braking). A representative set of parameters, selected to take into consideration every part of the driver-vehicle interaction, is associated to each of these events. Due to the usage of communication signals, influence factors, other than vehicle speed and acceleration (e.g. steering angle or pedals position), can be considered to determine the level of aggressiveness on the trip. The conversion of the parameters from physical values to dimensionless score is based on conversion maps that consider the road and vehicle types.
2016-04-05
Technical Paper
2016-01-1448
Rong Chen, Rini Sherony, Hampton C. Gabler
The effectiveness of Forward Collision Warning (FCW) or similar crash warning/mitigation systems is highly dependent on driver acceptance. If a FCW system delivers the warning too early, it may distract the driver or annoy the driver and cause him/her to deactivate the system. In order to design a system activation threshold that matches driver expectations, system designers must understand when drivers would normally apply the brake. One of the most widely used metrics to establish FCW threshold is Time to Collision (TTC). TTC measures the time remaining before two vehicle would collide if they continued at their current speeds. One limitation of TTC is that it assumes constant vehicle velocity. Enhanced Time to Collision (ETTC) is potentially a more accurate metric of perceived collision risk due to its consideration of vehicle acceleration. This paper will compare and contrast the population distribution of ETTC and TTC at brake onset in normal car-following situations.
2016-04-05
Technical Paper
2016-01-1412
Takeshi Hamaguchi, Satoshi Inoue, Shigeyuki Kimura, Terumasa Endo
In general, driver workload can be measured with questionnaires or other subjective methods for human-centered design. Many researchers have studied how subjective ratings of workload have good correspondence to physiological and/or behavioral, psychological measures. On the other hand, a model of driver behavior can be more informative because it allows researchers to estimate how drivers actually control the vehicle. Behavioral measures can be used to understand the interaction between a driver’s perception of information and his/her choice of action. Previously, pedal control was used for identifying specific individual habits or evaluating acceptability for a wide variety of driving assistance systems. Pedal behavior has not been modeled to estimate driver workload.
2016-04-05
Technical Paper
2016-01-1428
Bruce Mehler, Bryan Reimer, Jonathan Dobres, James Foley, Kazutoshi Ebe
This paper presents the results of a study of how people interacted with a production voice-command based interface while driving on public roadways. Tasks included phone contact calling, full address destination entry, and point-of-interest (POI). Baseline driving and driving while engaging in multiple-levels of an auditory-vocal cognitive reference task and manual radio tuning were used as comparison points. Measures included self-reported workload, task performance, physiological arousal, glance behavior, and vehicle control for an analysis sample of 48 participants (gender balanced across ages 21-68). Task analysis and glance measures confirm earlier findings that voice-command interfaces do not always allow the driver to keep their hands on the wheel and eyes on the road, as some assume.
2016-04-05
Technical Paper
2016-01-0141
Prasanna Vasudevan, Sreegururaj Jayachander
The sense of smell has been strongly linked to taste through direct chemical mechanisms. Its role in affecting human moods is a more complex phenomenon involving both chemical and psychological processes. Several studies using subjective responses to gauge the nature and influence of odors have attempted to throw light on the details of these processes. It is also a well-known fact that a large percentage of the commerce and trade powering global economics is facilitated by logistics through road transport networks. As distant producer – consumer connections are made, the drivers at the helms of the commercial transport vehicles make longer trips. This results in increased fatigue and risk of accidents. Work in the area of the effect of odors on alleviating the driver fatigue is limited. This paper shall describe, in detail and in particular, the effect of different odors typically obtainable in India.
2016-04-05
Technical Paper
2016-01-1425
Thomas McWilliams, Daniel Brown, Bryan Reimer, Bruce Mehler, Jonathan Dobres
Changes in physiological and operational behavior following lane departure warnings are explored in current production automotive systems. Different implementations employing auditory and haptic lane departure warning alerts were recorded in highway driving conditions from the factory installed lane departure warning systems. The lane departure warning events took place during single task driving periods as well as dual task driving. Dual task periods consisted of the driver interacting with the vehicle’s production interface to perform a secondary visual-manual (e.g., radio tuning, contact dialing, etc.) or auditory-vocal (e.g. destination address entry, contact dialing, etc.) tasks. Driver physiology and behavior were recorded and analyzed for pre-event and post-event conditions. To normalize between vehicles, percentage changes between pre-event and post-event measures were calculated.
2016-04-05
Technical Paper
2016-01-1421
Sean Seaman, Li Hsieh, Richard Young
This study investigated driver glances while engaging in infotainment tasks in a stationary vehicle while surrogate driving: watching a driving video recorded from a driver’s viewpoint and projected on a large screen, performing a lane-tracking task, and performing the Tactile Detection Response Task (TDRT) to measure attentional effects of secondary tasks on event detection and response. Twenty-four participants were seated in a 2014 Toyota Corolla production vehicle with the navigation system option. They performed the lane-tracking task using the vehicle’s steering wheel, fitted with a laser pointer to indicate wheel movement on the driving video. Participants simultaneously performed the TDRT and a variety of infotainment tasks, including Manual and Mixed-Mode versions of Destination Entry and Cancel, Contact Dialing, Radio Tuning, Radio Preset selection, and other Manual tasks. Participants also completed the 0- and 1-Back pure auditory-vocal tasks.
2016-04-05
Technical Paper
2016-01-1442
David Miller, Mishel Johns, Hillary Page Ive, Nikhil Gowda, David Sirkin, Srinath Sibi, Brian Mok, Sudipto Aich, Wendy Ju
Age and experience influence driver ability to cope with transitions between automated control and driver control, especially when drivers are engaged in media use. This study evaluated three age cohorts (young/new drivers, adults, and seniors) on their performance in transitions from automated driving to manual vehicle control in a full-vehicle driving simulator. Drivers were given three tasks to perform during the automated driving segments: to watch a movie on a tablet, to read a book section on a tablet, or to supervise the car's driving. We did not find significant differences in participants' accident avoidance ability following the different tasks.
2016-04-05
Technical Paper
2016-01-1426
Lex Fridman, Joonbum Lee, Bryan Reimer, Bruce Mehler
The challenge of developing a robust, real-time driver gaze classification system is that it has to handle difficult edge cases that arise in real-world driving conditions: extreme lighting variations, eyeglass reflections, sunglasses and other occlusions. We propose a single-camera end-to-end framework for classifying driver gaze into a discrete set of regions. This framework includes data collection, semi-automated annotation, offline classifier training, and an online real-time image processing pipeline that classifies the gaze region of the driver. We evaluate an implementation of each component on various subsets of a large on-road dataset. The key insight of our work is that robust driver gaze classification in real-world conditions is best approached by leveraging the power of supervised learning to generalize over the edge cases present in large annotated on-road datasets.
2016-04-05
Technical Paper
2016-01-1449
Taylor Johnson, Rong Chen, Rini Sherony, Hampton C. Gabler
Road departure crashes are one of the most dangerous crash modes in the United States. When the vehicle drifts out of lane and departs the roadway, it has a higher potential of impacting less compliant objects, such as trees, poles, as well as oncoming vehicles. In the U.S., road departure crashes account for 10% of all crashes, but is responsible for over 30% of all vehicle occupant fatalities. Lane departure warning (LDW) systems can detect an impending road departure and deliver an alert to allow the driver to steer back to the lane. LDW has great potential to reduce the number of road departure crashes, but the effectiveness is highly dependent upon driver acceptance. However, if the driver perceives there is little danger after receiving an alert, the driver may become annoyed and deactivate the system. Most current LDW systems rely heavily upon distance to lane boundary (DTLB) in the decision to deliver an alert.
2016-04-05
Technical Paper
2016-01-0147
Toshiya Hirose, Tomohiro Makino, Masanobu Taniguchi, Hidenobu Kubota
1. Background of this study Vehicle to vehicle communication system (V2V) can send and receive the vehicle information by wireless communication, and use as a safety driving assist for driver. Currently, requirements of technical guideline have been studied in Japan. In particular, it is investigated to clarify appropriate activation timing for collision information, caution and warning. This study focused on the activation timing of collision information with V2V, and investigated an effective activation timing of collision information. In addition, this study investigated the relationship between the activation timing and the accuracy of the vehicle position. 2. Experimental method This experiment used Driving Simulator. The experimental scenario is four situations of (1) “Assist for braking”, (2) “Assist for accelerating”, (3) “Assist for right turn” and (4) “Assist for left turn” in blind intersection.
2016-04-05
Technical Paper
2016-01-1455
John Gaspar, Timothy Brown, Chris Schwarz, Susan Chrysler, Pujitha Gunaratne
In 2010 more than 32,500 fatalities and over 2.2 million injuries occurred in automobile accidents, not to mention the immense economic impact on our society. Two of the four most frequent types of crashes are rear-end and lane change crashes. In 2011, rear-end crashes accounted for approximately 28% of all crashes while lane change crashes accounted for approximately 9%. In order to develop effective crash avoidance systems, we investigate incorporating driver response models to actuate the systems in a timely manner. Good models of driver behavior will support the development of algorithms that can detect normal and abnormal behavior as well as warning systems that are tuned to issue useful alerts that are not perceived as false, or nuisance, alerts by the driver. This paper documents a study on the NADS-1 driving simulator to support the development of such driver behavior modeling. Several scenario events were designed to fill in gaps left by previous crash research.
2016-04-05
Technical Paper
2016-01-1427
Richard Young, Li Hsieh, Sean Seaman
The Dimensional Model of Driver Demand is extended to include auditory-vocal (i.e., pure “voice” tasks), and Mixed-Mode tasks (i.e., a combination of auditory-vocal mode with visual-only, or with Visual-Manual modes). The extended model was validated with data from 24 participants using the 2014 Toyota Corolla infotainment system in a video-based surrogate driving venue. Twenty-two driver performance metrics were collected, including total eyes-off-road time (TEORT), mean single glance duration (MSGD), and proportion of long single glances (LGP). Other key metrics included response time (RT) and miss rate to a Tactile Detection Response Task (TDRT). The 22 metrics were simplified using Principal Component Analysis to two dimensions. The major dimension, explaining 60% of total variance, we interpret as the attentional effects of cognitive demand. The minor dimension, explaining 20% of total variance, we interpret as physical demand.
2016-04-05
Technical Paper
2016-01-1423
Richard Young, Sean Seaman, Li Hsieh
Many metrics have been used in an attempt to predict the effects of secondary tasks on driving behavior. Such metrics often give rise to seemingly paradoxical results, with one metric suggesting increased demand and another metric suggesting decreased demand for the same task. For example, for some tasks, drivers maintain their lane well yet detect events relatively poorly. For other tasks, drivers maintain their lane relatively poorly yet detect events relatively well. These seeming paradoxes are not time-accuracy trade-offs or experimental artifacts, because for other tasks, drivers do both well. The paradoxes are resolved if driver demand is modeled in two orthogonal dimensions rather than a single “driver workload” dimension. Principal components analysis (PCA) was applied to the published data from four simulator, track, and open road studies of visual-manual secondary task effects on driving.
2016-04-05
Journal Article
2016-01-1303
Haiqing Xu, Chang JIN, HONG ZHOU, YI ZHOU
On the study of reducing the disturbance on driver’s attention induced by low frequency vehicle interior stationary noise, a subjective evaluation is firstly carried out by means of rank rating method which introduces Distraction Level as evaluation index. A visual-finger response test is developed to help evaluating person better recognize the Distraction Level during the evaluation. A non-linear BP neural network is then modeled for the prediction of subjective Distraction Level, in which linear sound pressure RMS amplitudes of five critical bands from 20 to 500Hz are selected as inputs of the model. Furthermore, active noise equalization (ANE) on Distraction Level is realized based on FXLMS algorithm that controls the five gain coefficients of each input of trained BP neural network model.
2016-04-05
Technical Paper
2016-01-1420
Shinichi Kojima, Shigeyoshi Hiratsuka, Nobuyuki Shiraki, Kazunori Higuchi, Toshihiko Tsukada, Keiichi Shimaoka, Kazuya Asaoka, Sho Masuda, Kazuhiko Nakashima
The purpose of this study is to develop the projection pattern which is capable to shorten the driver’s perception time to night pedestrian than illuminating only high beam. Our approach is based on spatio-temporal frequency characteristics of human vision. Visual contrast sensitivity is dependent on spatio-temporal frequency, and maximum contrast sensitivity frequency is adapted by environmental luminance. Conventionally, there are some applications of spatio-temporal frequency characteristics of human vision such as NTSC television format. These were applied the low sensitivity of visual characteristics. By contrast, our approach applies the high sensitivity of visual characteristics. On the assumption that higher contrast sensitivity of spatio-temporal frequency is correlated with shorter perception time, we conducted an experiment to determine the frequency of maximum contrast sensitivity under lighting conditions that simulate night time light levels.
2016-04-05
Technical Paper
2016-01-0456
Zhaozhong Zhang, Dongpu Cao
This paper analyses how a human driver interacts with the steer-by-wire (SBW) controller using a simplified driver-vehicle-SBW system model. Driver model includes three main parameters: driver preview time, driver time delay and driver control gain. Driver neuromuscular dynamics are also considered using a simple transfer function. Simulation analyses and parametric studies have been conducted to draw conclusions for offering valuable information for SBW control design when considering driver-SBW collaborations.
2015-04-14
Journal Article
2015-01-1213
Zifan Liu, Andrej Ivanco, Zoran Filipi
Abstract This paper presents a new way to evaluate vehicle speed profile aggressiveness, quantify it from the perspective of the rapid speed fluctuations, and assess its impact on vehicle fuel economy. The speed fluctuation can be divided into two portions: the large-scale low frequency speed trace which follows the ongoing traffic and road characteristics, and the small-scale rapid speed fluctuations normally related to the driver's experience, style and ability to anticipate future events. The latter represent to some extent the driver aggressiveness and it is well known to affect the vehicle energy consumption and component duty cycles. Therefore, the rapid speed fluctuations are the focus of this paper. Driving data collected with the GPS devices are widely adopted for study of real-world fuel economy, or the impact on electrified vehicle range and component duty cycles.
2015-04-14
Technical Paper
2015-01-1407
Toshiya Hirose, Dai Kitabayashi, Hidenobu Kubota
Abstract This study investigated the driving characteristics of drivers when the system changes from autonomous driving to manual driving in the case of low driver alertness. The analysis clarified the difference in driving characteristics between cases of normal and low driver alertness. In the experiments, driver's alertness states varied from completely alert (level 1) to asleep (level 5). The experimental scenario was that the host vehicle drives along a highway at 27.8 m/s (100km/h) under the control of the autonomous system. The operation of the autonomous system is suspended, and the mode of autonomous driving changes to a mode of manual driving as the other vehicle pulls in front of the host vehicle. The driver then avoids a collision with the other vehicle with him/herself in control. The alertness level of drivers was determined from a previously developed method of examining video of the driver's face and their actions.
2015-04-14
Technical Paper
2015-01-0256
Changbo Fu, Paul (Tim) Freeman, John R. Wagner
Abstract Driver modeling is essential to both vehicle design and control unit development. It can improve the understanding of human driving behavior and decrease the cost and risk of vehicle system verification and validation. In this paper, three driver models were implemented to simulate the behavior of drivers subject to a run-off-road recovery event. Target path planning, pursuit behavior, compensate behavior, physical limitations, and neuromuscular modeling were taken into consideration in the feedforward/feedback driver model. A transfer function driver model and a cost function based driver model from a popular vehicle simulation software were also simulated and a comparison of these three models was made. The feedforward/feedback driver model exhibited the best balance of performance with smallest overshoot (0.226m), medium settling time (1.20s) and recovery time (4.30s).
2015-04-14
Technical Paper
2015-01-0259
Tyler Zellmer, Julio Rodriguez, John R. Wagner, Kim Alexander, Philip Pidgeon
Abstract According to the National Highway Traffic Safety Administration (NHTSA), motor collisions account for nearly 2.4 million injuries and 37 thousand fatalities each year in the United States. A great deal of research has been done in the area of vehicular safety, but very little has been completed to ensure licensed drivers are properly trained. Given the inherent risks in driving itself, the test for licensure should be uniform and consistent. To address this issue, an inexpensive, portable data acquisition and analysis system has been developed for the evaluation of driver performance. A study was performed to evaluate the system, and each participant was given a normalized driver rating. The average driver rating was μ=55.6, with a standard deviation of σ=12.3. All but 3 drivers fell into the so-called “Target Zone”, defined by a Driver Rating of μ± 1σ.
2015-04-14
Technical Paper
2015-01-1411
Caroline Crump, David Cades, Robert Rauschenberger, Emily Hildebrand, Jeremy Schwark, Brandon Barakat, Douglas Young
Abstract Advanced Driver Assistive System (ADAS) technologies have been introduced as the automotive industry moves towards autonomous driving. One ADAS technology with the potential for substantial safety benefits is forward collision warning and mitigation (FCWM), which is designed to warn drivers of imminent front-end collisions, potentiate driver braking responses, and apply the vehicle's brakes autonomously. Although the proliferation of FCWM technologies can, in many ways, mitigate the necessity of a timely braking response by a driver in an emergency situation, how these systems affect a driver's overall ability to safely, efficiently, and comfortably operate a motor vehicle remains unclear. Exponent conducted a closed-course evaluation of drivers' reactions to an imminent forward collision event while driving an FCWM-equipped vehicle, either with or without a secondary task administered through a hands-free cell phone.
2014-04-01
Technical Paper
2014-01-0434
Nicholas P. Skinner, John D. Bullough
Abstract Rear automotive lighting systems employing dynamic features such as sweeping or flashing are not commonly used on vehicles in North America, in part because they are not clearly addressed in vehicle lighting regulations. Nor is there abundant evidence suggesting they have a substantial role to play in driver safety. The results of a human factors investigation of the potential impacts of dynamic rear lighting systems on driver responses are summarized and discussed in the context of safety, visual effectiveness and the present regulatory context.
2014-04-01
Technical Paper
2014-01-0450
Tobias Karlsson, Magdalena Lindman, Jordanka Kovaceva, Bo Svanberg, Henrik Wiberg, Lotta Jakobsson
Abstract Different types of driver workload are suggested to impact driving performance. Operating a vehicle in a situation where the driver feel uneasy is one example of driver workload. In this study, passenger car driving data collected with Naturalistic Driving Study (NDS) data acquisition equipment was analyzed, aiming to identify situations corresponding to a high driver's subjective rating of ‘unease’. Data from an experimental study with subjects driving a passenger car in normal traffic was used. Situations were rated by the subjects according to experienced ‘unease’, and the Controller Area Network (CAN) data from the vehicle was used to describe the driving conditions and identify driving patterns corresponding to the situations rated as ‘uneasy’. These driving patterns were matched with the data in a NDS database and the method was validated using video data. Two data mining approaches were applied.
2014-04-01
Journal Article
2014-01-0448
Richard Young
This study reanalyzes the data from a recent experimental report from the University of Utah investigating the effect on driving performance of auditory-vocal secondary tasks (such as cell phone and passenger conversations, speech-to-text, and a complex artificial cognitive task). The current objective is to estimate the relative risk of crashes associated with such auditory-vocal tasks. Contrary to the Utah study's assumption of an increase in crash risk from the attentional effects of cognitive load, a deeper analysis of the Utah data shows that driver self-regulation provides an effective countermeasure that offsets possible increases in crash risk. For example, drivers self-regulated their following distances to compensate for the slight increases in brake response time while performing auditory-vocal tasks. This new finding is supported by naturalistic driving data showing that cell phone conversation does not increase crash risk above that of normal baseline driving.
2014-04-01
Technical Paper
2014-01-0236
Maki Kawakoshi, Takanobu Kaneko, Toru Nameki
Abstract Controllability (C) is the parameter that determines the Automotive Safety Integrity Level (ASIL) of each hazardous event based on an international standard of electrical and/or electronic systems within road vehicles (ISO 26262). C is classified qualitatively in ISO 26262. However, no specific method for classifying C is described. It is useful for C classification to define a specific classification based on objective data. This study assumed that C was classified using the percentage of drivers who could reduce Severity (S) in one or more classes compared with the S class in which the driver did not react to a hazardous event. An experiment simulated a situation with increased risk of collision with a leading vehicle due to insufficient brake force because of brake-assist failure when the experiment vehicle decelerated from 50 km/h on a straight road.
2014-04-01
Technical Paper
2014-01-0431
John D. Bullough
Abstract Present standards for vehicle forward lighting specify two headlamp beam patterns: a low beam when driving in the presence of other nearby vehicles, and a high beam when there is not a concern for producing glare to other drivers. Adaptive lighting technologies such as curve lighting systems with steerable headlamps may be related to increments in safety according to the Insurance Institute for Highway Safety, but isolating the effects of lighting is difficult. Recent analyses suggest that visibility improvements from adaptive curve lighting systems might reduce nighttime crashes along curves by 2%-3%. More advanced systems such as adaptive high-beam systems that reduce high-beam headlamp intensity toward oncoming drivers are not presently allowed in the U.S. The purpose of the present study is to analyze visual performance benefits and quantify potential safety benefits from adaptive high-beam headlamp systems.
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