Toyota drivers' experiences with Dynamic Radar Cruise Control,Pre-Collision System,and Lane-Keeping Assist

IntroductionAdvanced crash avoidance and driver assistance technologies potentially can prevent or mitigate many crashes. Previous surveys with drivers have found favorable opinions for many advanced technologies; however, these surveys are not necessarily representative of all drivers or all systems. As the technologies spread throughout the vehicle fleet, it is important to continue studying driver acceptance and use of them.MethodThis study focused on 2010–2013 Toyota Sienna and Prius models that were equipped with adaptive cruise control, forward collision avoidance, and lane departure warning and prevention (Prius models only). Telephone interviews were conducted in summer 2013 with 183 owners of vehicles with these technologies.ResultsAbout 9 in 10 respondents wanted adaptive cruise control and forward collision avoidance on their next vehicle, and 71% wanted lane departure warning/prevention again. Males and females reported some differences in their experiences with the systems; for example, males were more likely to have turned on lane departure warning/prevention than females, and when using this system, males reported more frequent warnings than did females. Relative to older drivers, drivers age 40 and younger were more likely to have seen or heard a forward collision warning.ConclusionsConsistent with the results in previous surveys of owners of luxury vehicles, the present survey found that driver acceptance of the technologies was high, although less so for lane departure warning/prevention. Experiences with the Toyota systems differed by driver age and gender to a greater degree than in previous surveys, suggesting that the responses of drivers may begin to differ as crash avoidance technology becomes available on a wider variety of vehicles.Practical applicationCrash avoidance technologies potentially can prevent or mitigate many crashes, but their success depends in part on driver acceptance. These systems will be effective only to the extent that drivers use them.     

How do drivers avoid collisions? A driving simulator-based study

Introduction: Drivers' collision avoidance performance in an impending collision situation plays a decisive role for safety outcomes. This study explored drivers' collision avoidance performances in three typical collision scenarios that were right-angle collision, head-on collision, and collision with pedestrian. Method: A high-fidelity driving simulator was used to design the scenarios and conduct the experiment. 45 participants took part in the simulator experiment. Drivers' longitudinal/lateral collision avoidance performances and collision result were recorded. Results: Experimental results showed that brake only was the most common response among the three collision scenarios, followed by brake combining swerve in head-on and pedestrian collision scenarios. In right-angle collision scenario with TTC (time to collision) largest among three scenarios, no driver swerved, and meanwhile drivers who showed slow brake reaction tended to compensate the collision risk by taking a larger maximum deceleration rate within a shorter time. Swerve-toward-conflict was a prevalent phenomenon in head-on and pedestrian collision scenarios and significantly associated with collision risk. Drivers that swerved toward the conflict object had a shorter swerve reaction time than drivers that swerved away from conflict. Conclusions: Long brake reaction time and wrong swerve direction were the main factors leading to a high collision likelihood. The swerve-toward-conflict maneuver caused a delay in brake action and degraded subsequent braking performances. The prevalent phenomenon indicated that drivers tended to use an intuitive (heuristic) way to make decisions in critical traffic situations. Practical applications: The study generated a better understanding of collision development and shed lights on the design of future advanced collision avoidance systems for semi-automated vehicles. Manufactures should also engage more efforts in developing active steering assistance systems to assist drivers in collision avoidance.     

Effect prediction of time-gaps for adaptive cruise control (ACC) and in-vehicle tasks on bus driver performance

Researchers have devoted a great deal of attention to the effects of driver assist systems on driver performance. This article describes a modeling approach to simulate the effects of time-gaps for adaptive cruise control (ACC) and manual in-vehicle tasks on bus-driver performance. A concept model was built with the knowledge of modularization, parameterization, and parallel processing. By running the model, the predictions for the effects of five levels of time-gaps and two types of in-vehicle tasks were collected in three measures: (1) mean gap, (2) minimum gap, and (3) collision rate. The model performed well in prediction, especially when driving with in-vehicle tasks. Predictions from the model were validated by the experiment with a verified fixed-base bus-driving simulator, used in the authors’ previous studies. Throughout the modeling approach, this research provides a theoretical and accurate way to assess effects of time-gaps and vehicle-equipped interfaces. In follow-up research, the authors will apply this approach to evaluate other driving assist systems (e.g. collision warning systems and navigation systems) to create a customized software kit.     

Analysis of effects of driver’s evasive action time on rear-end collision risk using a driving simulator

Introduction: Driver’s evasive action is closely associated with collision risk in a critical traffic event. To quantify collision risk, surrogate safety measures (SSMs) have been estimated using vehicle trajectories. However, vehicle trajectories cannot clearly capture presence and time of driver’s evasive action. Thus, this study determines the driver’s evasive action based on his/her use of accelerator and brake pedals, and analyzes the effects of the driver’s evasive action time (i.e., duration of evasive action) on rear-end collision risk. Method: Fifty drivers’ car-following behavior on a freeway was observed using a driving simulator. An SSM called “Deceleration Rate to Avoid Crash (DRAC)” and the evasive action time were determined for each driver using the data from the driving simulator. Each driver tested two traffic scenarios – Cars and Trucks scenarios where conflicting vehicles were cars and trucks, respectively. The factors related to DRAC were identified and their effects on DRAC were analyzed using the Generalized Linear Models and random effects models. Results: DRAC decreased with the evasive action time and DRAC was closely related to drivers’ gender and driving experience at the road sections where evasive action to avoid collision was required. DRAC was also significantly different between Cars and Trucks scenarios. The effect of the evasive action time on DRAC varied among different drivers, particularly in the Trucks scenario. Conclusions: Longer evasive action time can significantly reduce crash risk. Driver characteristics are more closely related to effective evasive action in complex driving conditions. Practical Applications: Based on the findings of this study, driver warning information can be developed to alert drivers to take specific evasive action that reduces collision risk in a critical traffic event. The information is likely to reduce the variability of the driver’s evasive action and the speed variations among different drivers.     

Human error taxonomies applied to driving: A generic driver error taxonomy and its implications for intelligent transport systems

Recent research indicates that driver error contributes to up to 75% of all roadway crashes. Despite this, only relatively little is currently known about the types of errors that drivers make and of the causal factors that contribute to these errors being made. This article presents an overview of the literature on human error in road transport. In particular, the work of three pioneers of human error research, Norman, Reason and Rasmussen, is scrutinised. An overview of the research on driver error follows, to consider the different types of errors that drivers make. It was found that all but one of these does not use a human error taxonomy. A generic driver error taxonomy is therefore proposed based upon the dominant psychological mechanisms thought to be involved. These mechanisms are: perception, attention, situation assessment, planning, and intention, memory and recall, and action execution. In addition, a taxonomy of road transport error causing factors, derived from the review of the driver error literature, is also presented. In conclusion to this article, a range of potential technological solutions that could be used to either prevent, or mitigate, the consequences of the driver errors identified are specified.     

考虑驾驶员反应时间的车辆碰撞预警模型

为了保证车辆在行驶过程中的安全性,提出了一种考虑驾驶员反应时间的车辆碰撞预警模型,改进了传统模型中驾驶员反应时间定值化的缺点。首先,依据车辆的制动过程分析了驾驶员反应时间对制动距离的影响。其次,设计驾驶员反应时间的模糊推理算法,选取驾龄、疲劳强度和应变能力3个主要因素作为评价指标来计算反应时间。最后,采用分等级的预警策略建立考虑驾驶员反应时间的碰撞预警模型,并通过Carsim-Matlab/Simulink联合仿真与传统模型进行对比分析。结果表明,设计的预警模型可以对不同类型的驾驶员进行差异化碰撞预警,在30 km/h和80 km/h两种车速下实际停车距离与理论值的最大误差为8%。     

Fatal crash between a car operating with automated control systems and a tractor-semitrailer truck

Abstract

Objective: The objective of this investigation was to evaluate the interaction between an SAE level 2 automated vehicle and the driver, including the limitations imposed by the vehicle on the driver.

Methods: A case study of the first fatal crash involving a vehicle operating with an automated control system was performed using scene evidence, vehicle damage, and recorded data available from the vehicle, and information from both drivers, including experience, phone records, computer systems, and medical information, was reviewed.

Results: System performance data downloaded from the car indicated that the driver was operating it using the Traffic-Aware Cruise Control and Autosteer lane-keeping systems, which are automated vehicle control systems within Tesla’s Autopilot suite. As the car crested the hill, a tractor trailer began its left turn onto a crossing roadway. Although reconstruction of the crash determined that there was sufficient sight distance for both drivers to see each other and take action, neither responded to the circumstances leading to the collision. Further, based on the speeds of the vehicles and simulations of the truck’s path, the car driver had at least 10.4?s to detect the truck and take evasive action. Neither the car driver nor the Autopilot system changed the vehicle’s velocity.

?At the time of the crash, the system performance data indicated that the last driver interaction with the system was 1?min 51?s prior when the cruise control speed was set to 74?mph. The driver was operating the vehicle using the Autopilot system for 37 of the 41?min in the last trip. During this period, the vehicle detected the driver’s hands on the steering wheel for a total of 25?s; each time his hands were detected on the wheel was preceded by a visual alert or auditory warning.

Conclusions: The National Transportation Safety Board (NTSB) determined that the probable cause of the Williston, Florida, crash was the truck driver’s failure to yield the right of way to the car, combined with the car driver’s inattention due to overreliance on vehicle automation, which resulted in the car driver’s lack of reaction to the presence of the truck. Contributing to the car driver’s overreliance on the vehicle automation was the car’s operational design, which permitted the driver’s prolonged disengagement from the driving task and his use of the automation in ways inconsistent with guidance and warnings from the manufacturer.     

Effects of lead time of verbal collision warning messages on driving behavior in connected vehicle settings

IntroductionUnder the connected vehicle environment, vehicles will be able to exchange traffic information with roadway infrastructure and other vehicles. With such information, collision warning systems (CWSs) will be able to warn drivers with potentially hazardous situations within or out of sight and reduce collision accidents. The lead time of warning messages is a crucial factor in determining the effectiveness of CWSs in the prevention of traffic accidents. Accordingly, it is necessary to understand the effects of lead time on driving behaviors and explore the optimal lead time in various collision scenarios.MethodsThe present driving simulator experiment studied the effects of controlled lead time at 16 levels (predetermined time headway from the subject vehicle to the collision location when the warning message broadcasted to a driver) on driving behaviors in various collision scenarios.ResultsMaximum effectiveness of warning messages was achieved when the controlled lead time was within the range of 5 s to 8 s. Specifically, the controlled lead time ranging from 4 s to 8 s led to the optimal safety benefit; and the controlled lead time ranging from 5 s to 8 s led to more gradual braking and shorter reaction time. Furthermore, a trapezoidal distribution of warning effectiveness was found by building a statistic model using curve estimation considering lead time, lifetime driving experience, and driving speed.ConclusionsThe results indicated that the controlled lead time significantly affected driver performance.Practical applicationsThe findings have implications for the design of collision warning systems.     

Driver Behavior During Overtaking Maneuvers from the 100-Car Naturalistic Driving Study

Objective: Lane changes with the intention to overtake the vehicle in front are especially challenging scenarios for forward collision warning (FCW) designs. These overtaking maneuvers can occur at high relative vehicle speeds and often involve no brake and/or turn signal application. Therefore, overtaking presents the potential of erroneously triggering the FCW. A better understanding of driver behavior during lane change events can improve designs of this human–machine interface and increase driver acceptance of FCW. The objective of this study was to aid FCW design by characterizing driver behavior during lane change events using naturalistic driving study data.

Methods: The analysis was based on data from the 100-Car Naturalistic Driving Study, collected by the Virginia Tech Transportation Institute. The 100-Car study contains approximately 1.2 million vehicle miles of driving and 43,000 h of data collected from 108 primary drivers. In order to identify overtaking maneuvers from a large sample of driving data, an algorithm to automatically identify overtaking events was developed. The lead vehicle and minimum time to collision (TTC) at the start of lane change events was identified using radar processing techniques developed in a previous study. The lane change identification algorithm was validated against video analysis, which manually identified 1,425 lane change events from approximately 126 full trips.

Results: Forty-five drivers with valid time series data were selected from the 100-Car study. From the sample of drivers, our algorithm identified 326,238 lane change events. A total of 90,639 lane change events were found to involve a closing lead vehicle. Lane change events were evenly distributed between left side and right side lane changes. The characterization of lane change frequency and minimum TTC was divided into 10 mph speed bins for vehicle travel speeds between 10 and 90 mph. For all lane change events with a closing lead vehicle, the results showed that drivers change lanes most frequently in the 40–50 mph speed range. Minimum TTC was found to increase with travel speed. The variability in minimum TTC between drivers also increased with travel speed.

Conclusions: This study developed and validated an algorithm to detect lane change events in the 100-Car Naturalistic Driving Study and characterized lane change events in the database. The characterization of driver behavior in lane change events showed that driver lane change frequency and minimum TTC vary with travel speed. The characterization of overtaking maneuvers from this study will aid in improving the overall effectiveness of FCW systems by providing active safety system designers with further understanding of driver action in overtaking maneuvers, thereby increasing system warning accuracy, reducing erroneous warnings, and improving driver acceptance.     

Age-related differences in effects of non-driving related tasks on takeover performance in automated driving

Introduction: During SAE level 3 automated driving, the driver’s role changes from active driver to fallback-ready driver. Drowsiness is one of the factors that may degrade driver’s takeover performance. This study aimed to investigate effects of non-driving related tasks (NDRTs) to counter driver’s drowsiness with a Level 3 system activated and to improve successive takeover performance in a critical situation. A special focus was placed on age-related differences in the effects. Method: Participants of three age groups (younger, middle-aged, older) drove the Level 3 system implemented in a high-fidelity motion-based driving simulator for about 30 min under three experiment conditions: without NDRT, while watching a video clip, and while switching between watching a video clip and playing a game. The Karolinska Sleepiness Scale and eyeblink duration measured driver drowsiness. At the end of the drive, the drivers had to take over control of the vehicle and manually change the lane to avoid a collision. Reaction time and steering angle variability were measured to evaluate the two aspects of driving performance. Results: For younger drivers, both single and multiple NDRT engagements countered the development of driver drowsiness during automated driving, and their takeover performance was equivalent to or better than their performance without NDRT engagement. For older drivers, NDRT engagement did not affect the development of drowsiness but degraded takeover performance especially under the multiple NDRT engagement condition. The results for middle-aged drivers fell at an intermediate level between those for younger and older drivers. Practical Applications: The present findings do not support general recommendations of NDRT engagement to counter drowsiness during automated driving. This study is especially relevant to the automotive industry’s search for options that will ensure the safest interfaces between human drivers and automation systems.     

Higher-order driving instruction and opportunities for improvement: Exploring differences across learner driver experience

Introduction: Few studies have investigated what guidance occurs during the Learner phase of driving, particularly during formal lessons. The objective of this research was threefold: (a) investigate functional and higher-order driving instruction (HO-DI) in formal Learner lessons, (b) explore teaching approaches within the context of a theoretical framework, and (c) investigate variation in these three elements of instruction as a function of Learner driving experience. The theoretical framework developed to guide this research integrated the constructivist Goals for Driver Education and self-determination theory. Method: Professional instruction was explored through naturalistic observation; 15 instructors provided GoPro recordings of 110 driving lessons with Learners aged 16–19 years (n = 96) at varying levels of experience: Early (<20 logbook hours), Mid (21–70 h), and Late (71–>100 h). Results: Employing a previously-developed coding taxonomy, instructor guidance opportunities were identified as 15% HO-DI, 73% functional instruction, and 12% untaken or missed HO-DI. Functional instruction peaked in the Mid Phase, while HO-DI was prominent in the Early phase suggesting missed opportunities in the later phases when use of silence peaked. Some elements of self-determination theory’s needs-supportive model were readily identified in teaching approaches, such as feedback. Conclusions: An understanding of functional and HO-DI, including teaching strategies, was established within the context of an integrated theoretical framework, with different trajectories across Learner experience identified. Teaching strategies reflected constructivism and self-determination theory providing theoretical support for these frameworks to be applied in future driver training studies. Continued research efforts are needed to understand how best to balance functional and HO-DI to maximize young novice drivers’ learning prior to independent driving, particularly during the late Learner period. Practical Applications: Naturalistic observation of current HO-DI and teaching approaches supports the feasibility of integrating recommended improvements arising from the theoretical framework within current practice, with practical implications for improvements to industry training.     

Medical investigation after a motorcoach strikes the rear of a tractor semitrailer parked on the highway; 13 fatal

Abstract

Objective: The objective of this study was to examine the medical conditions of 2 commercial drivers and the effects of physical barriers to occupant egress in a crash involving a tractor trailer and a motorcoach in order to assess and identify the factors that caused the crash and had a significant effect on occupant extrication.

Methods: Physical evidence from the scene, video evidence, commercial driver information, phone records, toxicology findings, autopsy results, and personal medical information were reviewed.

Results: On October 23, 2016, at 5:16 a.m., a motorcoach carrying a driver and 42 passengers struck the rear of a stopped semitrailer occupied by its driver in the center-right lane of Interstate 10 at highway speed outside Palm Springs, California. The motorcoach driver and 12 passengers died; 11 passengers were seriously injured.

All traffic had been stopped on I-10 early that morning to allow electrical lines to be strung over the highway. Security camera footage showed that the truck arrived at the end of a traffic queue 2?min before traffic flow resumed. Physical evidence indicated that the truck’s parking brake was still engaged at the time of the collision about 2?min later. The truck driver had a body mass index (BMI) between 45.6 and 50?kg/m², which placed him at very high risk of moderate to severe obstructive sleep apnea; he also inaccurately recalled that he had been stopped for 20–25?min and had placed the vehicle in gear just before the collision.

The motorcoach driver was on the return leg of an overnight trip to a casino. Based on his phone records, known driving time, and security camera footage, at the time of the collision he had had 4?h of sleep opportunity in the preceding 35?h. There was no evidence that the motorcoach driver attempted any evasive action before the collision. In addition, postmortem testing revealed a hemoglobin A1C of 11.4%, indicating poorly controlled diabetes; this was apparently undiagnosed prior to the crash.

The motorcoach was equipped with a single loading door at the front of the vehicle; it was rendered inoperable by the collision. Emergency egress was initially carried out through the emergency exit windows, but they repeatedly swung shut, impeding passengers’ efforts to exit. Emergency responders eventually cut open the bus wall to create a larger means of egress. Overall, it took almost 3?h to extricate the occupants from the vehicle.

Conclusions: The National Transportation Safety Board (NTSB) determined that the probable cause of the accident was the truck driver’s falling asleep, most likely due to undiagnosed moderate-to-severe obstructive sleep apnea, and the motorcoach driver’s failure to identify the stopped truck as a hazard requiring evasive action, most likely as the result of fatigue. Additional easy-to-use emergency exits would have decreased the time to extricate the occupants.     

Forward collision warning based on a driver model to increase drivers’ acceptance

Abstract

Objective: Systems that can warn the driver of a possible collision with a vulnerable road user (VRU) have significant safety benefits. However, incorrect warning times can have adverse effects on the driver. If the warning is too late, drivers might not be able to react; if the warning is too early, drivers can become annoyed and might turn off the system. Currently, there are no methods to determine the right timing for a warning to achieve high effectiveness and acceptance by the driver. This study aims to validate a driver model as the basis for selecting appropriate warning times. The timing of the forward collision warnings (FCWs) selected for the current study was based on the comfort boundary (CB) model developed during a previous project, which describes the moment a driver would brake. Drivers’ acceptance toward these warnings was analyzed. The present study was conducted as part of the European research project PROSPECT (“Proactive Safety for Pedestrians and Cyclists”).

Methods: Two warnings were selected: One inside the CB and one outside the CB. The scenario tested was a cyclist crossing scenario with time to arrival (TTA) of 4?s (it takes the cyclist 4?s to reach the intersection). The timing of the warning inside the CB was at a time to collision (TTC) of 2.6?s (asymptotic value of the model at TTA = 4?s) and the warning outside the CB was at TTC = 1.7?s (below the lower 95% value at TTA = 4?s). Thirty-one participants took part in the test track study (between-subjects design where warning time was the independent variable). Participants were informed that they could brake any moment after the warning was issued. After the experiment, participants completed an acceptance survey.

Results: Participants reacted faster to the warning outside the CB compared to the warning inside the CB. This confirms that the CB model represents the criticality felt by the driver. Participants also rated the warning inside the CB as more disturbing, and they had a higher acceptance of the system with the warning outside the CB. The above results confirm the possibility of developing wellsaccepted warnings based on driver models.

Conclusions: Similar to other studies’ results, drivers prefer warning times that compare with their driving behavior. It is important to consider that the study tested only one scenario. In addition, in this study, participants were aware of the appearance of the cyclist and the warning. A further investigation should be conducted to determine the acceptance of distracted drivers.     

Applying online fleet driver assessment to help identify,target and reduce occupational road safety risks

Many studies have shown that driver attitude and behaviour are important determinants of the likelihood of collision involvement. Knowledge of the Rules of the Road and the perception of hazards are also associated with collision involvement. The aim of this paper is to review the practical application of an online fleet driver assessment program to help identify, target and reduce occupational road safety risks. A large and unique data set collected from online assessment of drivers employed in a UK telecommunications organisation is analyzed. Data was also collected on driver demographics and their driving and collision history. Analysis of the data revealed that attitude, behaviour, knowledge and hazard perception are highly correlated with self-reported collisions. The influence of these variables on collision involvement was assessed using a Poisson regression model. Both attitude and behaviour scores exhibit a statistically significant association with collision involvement, along with other variables such as mileage driven, driver age and personality. The findings lend support to the need to create a safety culture in which driver assessment and improvement is the norm, as well as reducing exposure to risk wherever possible through better ways of working and travelling.     

基于ABAQUS二次开发的头盔防碰撞仿真系统及应用

目前国内的航空头盔设计仍依赖于经验设计,设计出来的头盔冗余较大,且需要大量的实验进行验证,研制周期长,效率低。针对这些问题,笔者基于ABAQUS平台,并针对航空头盔的特殊性进行二次开发,得到了一款能够进行防碰撞仿真的系统。应用该系统,仿真了3种不同规格的头盔,3种不同装配误差的泡沫硬衬垫共计6种模型的碰撞结果。仿真结果对头盔的设计有一定的指导价值。     

Simulating the relative influence of tire,vehicle and driver factors on forward collision accident rates

Introduction: There is currently a strong focus within the automotive industry centered on traffic safety, with topics such as distracted driving, accident avoidance technologies, and autonomous vehicles. These papers tend to focus on the possible improvements from a single factor. However, there are many factors that are present in each accident, and it is important to understand the influence of each factor on the relative accident risk in order to identify the most effective approaches for improving driver safety. Rear-end accidents tend to be the most common accident type with approximately 1.8 M cases, or 31% of all accidents, in 2012, according to NHTSA. Of the rear-end accident scenarios, approximately 18–23% occur on wet surfaces. Method: A Monte Carlo Forward Collision Simulation models the conditions of a wet rear-end accident and estimates the relative impact of various vehicle collision parameters. The model takes distributions of these parameters as inputs, and outputs a risk of collision relative to a known reference case. The parameters that can be studied include: tire grip level, road grip level, vehicle velocity, following distances, and the presence of vehicle technologies (ABS, FCW & AEB). Distributions of some of these parameters have been improved thanks to Naturalistic Driving Study data from SHRP2. Results: This study shows that these vehicle systems have a large impact on safety and can change the amount of influence attributed to other parameters such as tire grip levels. As the use of automated vehicle systems expands, so will the influence of tire grip performance levels on collision risks. Practical Applications: It is more important than ever for consumers and auto manufacturers to consider tire performance levels. Therefore, the tire industry should continue to focus on wet grip as a key performance related to safety and should strive to continue to improve tire performance.     

Risk factors associated with truck-involved fatal crash severity: Analyzing their impact for different groups of truck drivers

Introduction: Fatal crashes that include at least one fatality of an occupant within 30 days of the crash cause large numbers of injured persons and property losses, especially when a truck is involved. Method: To better understand the underlying effects of truck-driver-related characteristics in fatal crashes, a five-year (from 2012 to 2016) dataset from the Fatality Analysis Reporting System (FARS) was used for analysis. Based on demographic attributes, driving violation behavior, crash histories, and conviction records of truck drivers, a latent class clustering analysis was applied to classify truck drivers into three groups, namely, ‘‘middle-aged and elderly drivers with low risk of driving violations and high historical crash records,” ‘‘drivers with high risk of driving violations and high historical crash records,” and ‘‘middle-aged drivers with no driving violations and conviction records.” Next, equivalent fatalities were used to scale fatal crash severities into three levels. Subsequently, a partial proportional odds (PPO) model for each driver group was developed to identify the risk factors associated with the crash severity. Results' Conclusions: The model estimation results showed that the risk factors, as well as their impacts on different driver groups, were different. Adverse weather conditions, rural areas, curved alignments, tractor-trailer units, heavier weights and various collision manners were significantly associated with the crash severities in all driver groups, whereas driving violation behaviors such as driving under the influence of alcohol or drugs, fatigue, or carelessness were significantly associated with the high-risk group only, and fewer risk factors and minor marginal effects were identified for the low-risk groups. Practical Applications: Corresponding countermeasures for specific truck driver groups are proposed. And drivers with high risk of driving violations and high historical crash records should be more concerned.     

Assessing drivers' response during automated driver support system failures with non-driving tasks

IntroductionWith the increase in automated driver support systems, drivers are shifting from operating their vehicles to supervising their automation. As a result, it is important to understand how drivers interact with these automated systems and evaluate their effect on driver responses to safety critical events. This study aimed to identify how drivers responded when experiencing a safety critical event in automated vehicles while also engaged in non-driving tasks.MethodIn total 48 participants were included in this driving simulator study with two levels of automated driving: (a) driving with no automation and (b) driving with adaptive cruise control (ACC) and lane keeping (LK) systems engaged; and also two levels of a non-driving task (a) watching a movie or (b) no non-driving task. In addition to driving performance measures, non-driving task performance and the mean glance duration for the non-driving task were compared between the two levels of automated driving.ResultsDrivers using the automated systems responded worse than those manually driving in terms of reaction time, lane departure duration, and maximum steering wheel angle to an induced lane departure event. These results also found that non-driving tasks further impaired driver responses to a safety critical event in the automated system condition.ConclusionIn the automated driving condition, driver responses to the safety critical events were slower, especially when engaged in a non-driving task.Practical applicationTraditional driver performance variables may not necessarily effectively and accurately evaluate driver responses to events when supervising autonomous vehicle systems. Thus, it is important to develop and use appropriate variables to quantify drivers' performance under these conditions.     

Socio-demographic and experience factors affecting drivers’ runoff risk along horizontal curves of two-lane rural highway

Introduction: An improper driving strategy is one of the causative factors for a high probability of runoff and overturning crashes along the horizontal curves of two-lane highways. The socio-demographic and driving experience factors of a driver do influence driving strategy. Hence, this paper explored the effect of these factors on the driver’s runoff risk along the horizontal curves. Method: The driving performance data of 48 drivers along 52 horizontal curves was recorded in a fixed-base driving simulator. The driving performance index was estimated from the weighted lateral acceleration profile of each driver along a horizontal curve. It was clustered and compared with the actual runoff events observed during the experiment. It yielded high, moderate, and low-risk clusters. Using cross-tabulation, each risk cluster was compared with the socio-demographic and experience factors. Further, generalized mixed logistic regression models were developed to predict the high-risk and high to moderate risk events. Results: The age and experience of drivers are the influencing factors for runoff crash. The high-risk event percentage for mid-age drivers decreases with an increase in driving experience. For younger drivers, it increases initially but decreases afterwards. The generalized mixed logistic regression models identified young drivers with mid and high experience and mid-age drivers with low-experience as the high-risk groups. Conclusions: The proposed index parameter is effective in identifying the risk associated with horizontal curves. Driver training program focusing on the horizontal curve negotiation skills and graduated driver licensing could help the high-risk groups. Practical applications: The proposed index parameter can evaluate driving behavior at the horizontal curves. Driving behavior of high-risk groups could be considered in highway geometric design. Motor-vehicle agencies, advanced driver assistance systems manufacturers, and insurance agencies can use proposed index parameter to identify the high-risk drivers for their perusal.     

Combining Coordination of Motion Actuators with Driver Steering Interaction

Objective: A new method is suggested for coordination of vehicle motion actuators; where driver feedback and capabilities become natural elements in the prioritization.

Methods: The method is using a weighted least squares control allocation formulation, where driver characteristics can be added as virtual force constraints. The approach is in particular suitable for heavy commercial vehicles that in general are over actuated. The method is applied, in a specific use case, by running a simulation of a truck applying automatic braking on a split friction surface. Here the required driver steering angle, to maintain the intended direction, is limited by a constant threshold. This constant is automatically accounted for when balancing actuator usage in the method.

Results: Simulation results show that the actual required driver steering angle can be expected to match the set constant well. Furthermore, the stopping distance is very much affected by this set capability of the driver to handle the lateral disturbance, as expected.

Conclusion: In general the capability of the driver to handle disturbances should be estimated in real-time, considering driver mental state. By using the method it will then be possible to estimate e.g. stopping distance implied from this. The setup has the potential of even shortening the stopping distance, when the driver is estimated as active, this compared to currently available systems. The approach is feasible for real-time applications and requires only measurable vehicle quantities for parameterization. Examples of other suitable applications in scope of the method would be electronic stability control, lateral stability control at launch and optimal cornering arbitration.