Learning from errors in a driving simulation: effects on driving skill and self-confidence

Two experiments investigated the effect of making errors during training (error training) on a driving simulator versus learning from examples of errors (guided error training) on driving skill and confidence. Experiment 1 indicated that compared with errorless learning (where participants drove through a training run not designed to elicit errors), error training led to significantly better transfer to driving tests that were analogous to those situations encountered in training and more effective use of strategies for coping with a novel driving situation. Error training also reduced self-confidence in driving skill at the end of training relative to errorless learning. Experiment 2 provided weak evidence of the superiority of guided error training over errorless learning (where the driver in the video did not make any errors) on analogous tests, and no evidence of transfer to a novel test. Furthermore, guided error training did not influence self-confidence in driving skill. The potential value of driving simulators in providing active processing during driver training is discussed, along with the effects of passive and active exposure to errors on driver confidence.     

Relationships between driving simulator performance and driving test results

This article is considered relevant because: 1) car driving is an everyday and safety-critical task; 2) simulators are used to an increasing extent for driver training (related topics: training, virtual reality, human – machine interaction); 3) the article addresses relationships between performance in the simulator and driving test results–a relevant topic for those involved in driver training and the virtual reality industries; 4) this article provides new insights about individual differences in young drivers' behaviour. Simulators are being used to an increasing extent for driver training, allowing for the possibility of collecting objective data on driver proficiency under standardised conditions. However, relatively little is known about how learner drivers' simulator measures relate to on-road driving. This study proposes a theoretical framework that quantifies driver proficiency in terms of speed of task execution, violations and errors. This study investigated the relationships between these three measures of learner drivers' (n = 804) proficiency during initial simulation-based training and the result of the driving test on the road, occurring an average of 6 months later. A higher chance of passing the driving test the first time was associated with making fewer steering errors on the simulator and could be predicted in regression analysis with a correlation of 0.18. Additionally, in accordance with the theoretical framework, a shorter duration of on-road training corresponded with faster task execution, fewer violations and fewer steering errors (predictive correlation 0.45). It is recommended that researchers conduct more large-scale studies into the reliability and validity of simulator measures and on-road driving tests.     

Relationships between driving simulator performance and driving test results

This article is considered relevant because: 1) car driving is an everyday and safety-critical task; 2) simulators are used to an increasing extent for driver training (related topics: training, virtual reality, human-machine interaction); 3) the article addresses relationships between performance in the simulator and driving test results--a relevant topic for those involved in driver training and the virtual reality industries; 4) this article provides new insights about individual differences in young drivers' behaviour. Simulators are being used to an increasing extent for driver training, allowing for the possibility of collecting objective data on driver proficiency under standardised conditions. However, relatively little is known about how learner drivers' simulator measures relate to on-road driving. This study proposes a theoretical framework that quantifies driver proficiency in terms of speed of task execution, violations and errors. This study investigated the relationships between these three measures of learner drivers' (n=804) proficiency during initial simulation-based training and the result of the driving test on the road, occurring an average of 6 months later. A higher chance of passing the driving test the first time was associated with making fewer steering errors on the simulator and could be predicted in regression analysis with a correlation of 0.18. Additionally, in accordance with the theoretical framework, a shorter duration of on-road training corresponded with faster task execution, fewer violations and fewer steering errors (predictive correlation 0.45). It is recommended that researchers conduct more large-scale studies into the reliability and validity of simulator measures and on-road driving tests.     

Comparative evaluation of performance measures for human driving skills

In this paper, we evaluate the adequacy of several performance measures for the evaluation of driving skills between different drivers. This work was motivated by the need for a training system that captures the driving skills of an expert driver and transfers the skills to novice drivers using a haptic-enabled driving simulator. The performance measures examined include traditional task performance measures, e.g., the mean position error, and a stochastic distance between a pair of hidden Markov models (HMMs), each of which is trained for an individual driver. The emphasis of the latter is on the differences between the stochastic somatosensory processes of human driving skills. For the evaluation, we developed a driving simulator and carried out an experiment that collected the driving data of an expert driver whose data were used as a reference for comparison and of many other subjects. The performance measures were computed from the experimental data, and they were compared to each other. We also collected the subjective judgement scores of the driver’s skills made by a highly-experienced external evaluator, and these subjective scores were compared with the objective performance measures. Analysis results showed that the HMM-based distance metric had a moderately high correlation between the subjective scores and it was also consistent with the other task performance measures, indicating the adequacy of the HMM-based metric as an objective performance measure for driving skill learning. The findings of this work can contribute to developing a driving simulator for training with an objective assessment function of driving skills.     

Analysis and modeling of human driving behaviors using adaptive cruise control

We describe a driver model based on the feedback-error learning scheme using neural network (NN) for adaptive cruise control (ACC) use in driving and show the applicability of the feedback-error learning scheme as a behavior model of adaptability of human. The focus of the study is on the adaptation process of driving behaviors using ACC. The driver model for computer simulations was implemented by using a NN. The developed simulation model is used for predicting control performance of skilled driver using ACC. In the experiments, we used a fixed-base driving simulator (DS) installed ACC system for collecting driver’s data. Headway time when lane changing in a row and lateral deviation from road center were investigated as driver behavior characteristics during ACC use and manual driving, respectively. The simulation results of the lateral deviation were compared with the experimental results and showed that control performance with ACC use would be better than that of manual driving. Furthermore, it was found that human error occurred during ACC use in the DS experiments and provided an explanation of the cause in terms of Neisser’s perceptual cycle model.     

司机分心驾驶检测研究进展

随着车辆工业和世界经济的快速发展,私家汽车数量不断增加,导致交通事故越来越多,且交通安全问题已经成为全球关注的焦点问题。司机分心驾驶检测的研究主要分为传统计算机视觉（CV）算法和深度学习算法两种。基于传统CV算法的司机分心检测通过尺度不变特征转换（SIFT）、方向梯度直方图（HOG）等特征算子提取图像特征,然后结合支持向量机（SVM）建立模型并对图像进行分类。然而传统CV算法具有对环境的要求高、运用范围较窄、参数多、计算量大的缺点。近年来深度学习在提取数据特征方面表现出速度快、精度高等优异的性能,因此研究人员开始将深度学习引入到司机分心驾驶检测中。基于深度学习的方法可以实现端到端的司机分心驾驶检测网络,而且取得了很高的准确度。介绍了传统CV算法和深度学习算法在司机分心驾驶检测的研究现状,首先,阐释了传统CV算法用于图像领域和司机分心驾驶检测研究的情况;接着,介绍了基于深度学习的司机分心驾驶研究;而后,从准确度、模型参数量等方面对不同司机分心驾驶检测方法进行比较分析;最后,对现有的研究进行了总结并提出了未来司机分心驾驶检测需要解决的三个问题：驾驶过程中司机分心状态以及分心程度划分规范需进一步完善,需要综合考虑人－车－路三者以及如何才能更有效地减少神经网络参数。     

Speed-of-processing and driving simulator training result in improved driving performance

Useful field of view, a measure of processing speed and spatial attention, can be improved with training. We evaluated the effects of this improvement on older adults' driving performance. Elderly adults participated in a speed-of-processing training program (N = 48), a traditional driver training program performed in a driving simulator (N = 22), or a low-risk reference group (N = 25). Before training, immediately after training or an equivalent time delay, and after an 18-month delay each participant was evaluated in a driving simulator and completed a 14-mile (22.5-km) open-road driving evaluation. Speed-of-processing training, but not simulator training, improved a specific measure of useful field of view (UFOV), transferred to some simulator measures, and resulted in fewer dangerous maneuvers during the driving evaluation. The simulator-trained group improved on two driving performance measures: turning into the correct lane and proper signal use. Similar effects were not observed in the speed-of-processing training or low-risk reference groups. The persistence of these effects over an 18-month test interval was also evaluated. Actual or potential applications of this research include driver assessment and/or training programs and cognitive intervention programs for older adults.     

Learning headway estimation in driving

OBJECTIVE: The main purpose of the present study was to examine to what extent the ability to attain a required headway of 1 or 2 s can be improved through practical driving instruction under real traffic conditions and whether the learning is sustained after a period during which there has been no controlled training. BACKGROUND: The failure of drivers to estimate headways correctly has been demonstrated in previous studies. METHODS: Two methods of training were used: time based (in seconds) and distance based (in a combination of meters and car lengths). For each method, learning curves were examined for 18 participants at speeds of 50, 80, and 100 km/hr. RESULTS: The results indicated that drivers were weak in estimating headway prior to training using both methods. The learning process was rapid for both methods and similar for all speeds; thus, after one trial with feedback, there was already a significant improvement. The learning was retained over time, for at least the 1 month examined in this study. CONCLUSION: Both the time and distance training of headway improved drivers' ability to attain required headways, with the learning being maintained over a retention interval. The learning process was based on perceptual cues from the driving scene and feedback from the experimenter, regardless of the formal training method. APPLICATION: The implications of these results are that all drivers should be trained in headway estimation using an objective distance measuring device, which can be installed on driver instruction vehicles.     

Effects of an in-vehicle collision avoidance warning system on short- and long-term driving performance

Many new in-vehicle systems focus on accident prevention by facilitating the driving task. One such driving aid is an in-vehicle collision avoidance warning system (IVCAWS), used to alert the driver to an impending collision. Our study evaluated the effects of an imperfect IVCAWS both on driver headway maintenance and on driver behavior in response to warning system errors. Our results showed that drivers tend to overestimate their headway and consequently drive with short and potentially dangerous headways, and that IVCAWSs are a useful tool for educating drivers to estimate headway more accurately. Moreover, our study showed that after a relatively short exposure to the system, drivers were able to maintain longer and safer headways for at least six months. The practical implications of these results are that the use of an IVCAWS should be considered for inclusion in driver education and training programs.     

Learning to make errors: evidence from a driving task simulation

Human errors represent a mismatch between the demands of an operational system and what the operator does. If they cannot be reversed, their consequences may be severe. Errors are frequently classified as design-or operator-induced. A third class of errors may also be identified, namely process-induced errors. Such errors arise out of on-going processes which typically extend over time. One such process is that of learning. In relation to the acquisition of skills, for example, learning frequently involves a trial-and-error component. Accidents by inexperienced drivers may represent a severe consequence of such errors. Errors may also arise out of particular learning experiences which provide a distorted underestimate of objective risk and/or motivate high risk behaviour. These phenomena are investigated in a computer simulation of the driving task. The relationship is discussed between various kinds of learning experience and the development of situations in which the possibility of error recovery declines. Some suggestions for reducing the frequency of irreversible errors and for increasing the data base for human error in vehicle driving are made.     

Aberrant driving behaviour: errors and violations

The present study was a replication of the research of Reason et al. (1990). Its aim was to confirm the distinction between driving errors and violations in a Western Australian driving population. Sixty-one male drivers and 74 female drivers completed a questionnaire containing items on driver demographics, driving penalties incurred, driving convictions and accident history and driver behavioural aberrations drawn from the Driver Behaviour Questionnaire (DBQ). In agreement with Reason et al. factor analysis revealed three factors; in the present study these were general errors, dangerous errors, and dangerous violations. Young drivers committed more dangerous errors and dangerous violations than older drivers. Females reported more dangerous errors than males. Males reported more dangerous violations than females. Drivers who reported a high level of road exposure and those who reported having been convicted for speeding reported more dangerous violations. Differences in the results of the two studies can largely by accounted for by differences in the representation of age and gender in the two populations studied.     

Using eye movements to evaluate a PC-based risk awareness and perception training program on a driving simulator

OBJECTIVE: Evaluation of the effects of a PC-based training program on risk perception in a driving simulator. BACKGROUND: Novice drivers have a fatality rate some eight times higher than that of the most experienced group of drivers, primarily because of the novice driver's inability to predict ahead of time the risks that will appear in the roadway. Current driver education programs, at least those in the United States, do not emphasize the teaching of risk awareness skills to novice drivers. METHOD: A PC-based risk awareness and perception training program was developed and evaluated. The training involved using plan (top-down) views of 10 risky scenarios that helped novice drivers identify where potential risks were located and what information should be attended. Both the 24 trained novice drivers and 24 untrained novice drivers were evaluated on an advanced driving simulator. The eye movements of both groups of drivers were measured. The evaluation on the driving simulator included both scenarios used in the training and others not used in training. RESULTS: The set of trained novice drivers were almost twice as likely as untrained drivers to fixate appropriately either on the regions where potential risks might appear or on signs that warned of potentially risky situations ahead, both for the scenarios they had encountered in training and for novel scenarios. APPLICATION: The PC training program developed, which is portable and can be widely used, has great promise in improving risk perception for novice drivers on the road.     

How eco-driving training course influences driver behavior and comprehensibility: a driving simulator study

Eco-driving behavior has been treated as one of the most cost-effective way in reducing vehicle fuel consumption and emissions. In this study, the effects of eco-driving training on different driving behaviors in start, stop, speed choice and no-idling from different levels of training, from receiving only static information (EDUCATION) to guided practicing (COACHING after EDUCATION), were thoroughly evaluated and compared. The drivers’ comprehensibility with eco-driving information was examined through eco-driving questionnaire surveys. The results indicated that EDUCATION alone was effective to improve driver’s comprehensibility with the basic concepts of eco-driving, to reduce the percentages of vehicle idling and to help drivers to avoid rapid starts slightly. EDUCATION alone did not significantly improve the driving behaviors of stop and speed choice, while COACHING after EDUCATION was found to enhance the effectiveness of these two elements of eco-driving. COACHING after EDUCATION did not introduce much additional benefit for more fuel-efficient starting pattern. In addition, eco-driving training led to more consistent driving behavior as evidenced by smaller standard deviation values on all measures related to fuel efficiency.     

On-line driver workload estimation. Effects of road situation and age on secondary task measures

In order to develop a driver-car interface that adapts the presentation of messages generated by in-vehicle information systems to driver workload, two experiments investigated potential determinants of driver visual and mental workload as indicated by performance on two secondary tasks. Experiment 1 suggested that road situation is a major determinant of visual and mental workload of the driver and that the processing resources of older drivers are somewhat more limited than those of younger and middle-aged drivers. Familiarity with the area of driving (when guided) and time of day (associated with traffic density) showed no secondary task effects. Experiment 2 showed that the categorization of road situations, proposed in Experiment 1, could underlie adaptation of visually loading messages to the workload incurred by driving. This was not found with respect to mentally loading messages.     

On-line driver workload estimation. Effects of road situation and age on secondary task measures

In order to develop a driver-car interface that adapts the presentation of messages generated by in-vehicle information systems to driver workload, two experiments investigated potential determinants of driver visual and mental workload as indicated by performance on two secondary tasks. Experiment 1 suggested that road situation is a major determinant of visual and mental workload of the driver and that the processing resources of older drivers are somewhat more limited than those of younger and middle-aged drivers. Familiarity with the area of driving (when guided) and time of day (associated with traffic density) showed no secondary task effects. Experiment 2 showed that the categorization of road situations, proposed in Experiment 1, could underlie adaptation of visually loading messages to the workload incurred by driving. This was not found with respect to mentally loading messages.     

A smartphone based technique to monitor driving behavior using DTW and crowdsensing

Safety issues while driving in smart cities are considered to be top-notch priority in contrast to traveling. Today’s fast paced society, often leads to accidents. In order to reduce the road accidents, one key area of research is monitoring the driving behavior of drivers. Understanding the driver behavior is an essential component in Intelligent Driver Assistance Systems. One of potential cause of traffic fatalities is aggressive driving behavior. However, drivers are not fully aware of their aggressive actions. So, in order to increase awareness and to promote driver safety, a novel system has been proposed. In this work, we focus on DTW based event detection technique, which have not been researched in motion sensors based time series data to a great extent. Our motivation is to improve the classification accuracy to detect sudden braking and aggressive driving behaviors using sensory data collected from smartphone. A very significant feature of DTW is to be able to automatically cope with time deformations and different speeds associated with time-dependent data which makes it suitable for our chosen application where data might get affected due to factors such as: high variability in road and vehicle conditions, heterogeneous smartphone sensors, etc. Our technique is novel as it uses fusion of sensors to enhance detection accuracy. The experimental results show that proposed algorithm outperforms the existing machine learning and threshold-based techniques with 100% detection rate of braking events and 97% & 86.67% detection rate of normal left & right turns and aggressive left & right turns respectively.     

Young novice driver subtypes: relationship to high-risk behavior, traffic accident record, and simulator driving performance

Two studies were undertaken to obtain empirical support for the existence of driver subtypes in the young novice driver population. In Study 1, 198 participants (55% male) aged 16 to 19 completed an extensive self-report questionnaire. Five novice driver subtypes were identified through a cluster analysis of personality and driving-related measures. Two relatively high-risk or deviant subtypes (Clusters 1 and 5) were identified, characterized by high levels of driving-related aggression, competitive speed, driving to reduce tension, sensation seeking, assaultiveness, and hostility. The individuals in Cluster 5 also reported low levels of emotional adjustment and high levels of depression, resentfulness, and irritability. In Study 2, a subset of participants from each of the subtypes drove several scenarios in a driving simulator. The subtypes differed in their responses to an emergency situation and several potential traffic hazards. They also differed in the proficiency with which they could control their attention among concurrent tasks in high-workload situations. Most of the significant differences were related to lower levels of driving skill among the two most deviant subtypes (Clusters 1 and 5). The potential applications of this research include the design of training programs and other countermeasures to address the young novice driver crash problem.     

Anticipating the content and circumstances of skill transfer: unrealistic expectations of driver training and graduated licensing?

There is substantial evidence that driving skills improve during driver training, but the long-term safety benefit of such formal training remains unproven. Restricting the exposure of newly licensed drivers to more hazardous driving circumstances, as in graduated driver licensing (GDL) regimes, demonstrably reduces crash risk, but drivers remain at risk after the restrictions are eased. GDL and most other licensing regimes advocate increased basic training and practice, but thereafter require neither advanced training nor systematic increase in exposure to risk. This assumes that basic skills acquired during formal training will transfer positively to new and more demanding traffic circumstances. This paper reviews the theoretical basis for these assumptions and offers a way of systematically identifying the extent of transfer desired. It is concluded that there is little theoretical or empirical foundation for the supposition that what is learned during or after training will have a safety benefit in later driving.     

The use of expert knowledge in the development of simulations for train driver training

The current study was undertaken to inform the development of simulations for improving train driver’s decision making under degraded track conditions. Trains are sophisticated heavy machinery and their performance is ever increasing resulting in the driving task becoming more complex and progressively dominated by cognitive and perceptual skills. A critical part of reducing the potential for train driver error and of increasing performance lies in the appropriate design of simulation training. In the current study a cognitive task analysis, using the critical decision method (CDM) was undertaken using a focus group research design. The process resulted in increased knowledge of expert train driver decision-making processes. Across four major incidents analyzed 11 decision points, 17 cues, 30 essential responsive actions and 45 possible errors where identified. The use of these results for supporting the design of simulation training and associated performance measures is discussed.     

基于车辆行驶数据的驾驶人行为谱分析方法

针对我国驾驶人行为谱的研究尚不完善，专业领域内没有相应的行为谱分析工具的问题，提出了一套针对营运客车的完整的驾驶人驾驶行为谱体系并设计了一套分析工具。首先，设计并定义了驾驶人行为谱的特征指标和评价指标；其次，给出了驾驶人行为谱的特征指标分析、计算方法，采用基于马尔可夫链蒙特卡洛采样和离群点剔除的K-means算法对驾驶人的驾驶风格进行分析，采用回归学习对驾驶人的驾驶技能进行分析；然后，设计了基于车联网、大数据的驾驶人行为谱的基础数据采集和预处理方法；最后，采用Java语言、Spring MVC架构开发出驾驶人行为谱分析工具。将机器学习中的数据挖掘、数据分析算法与交通安全领域相结合，对完善我国驾驶人行为谱框架体系具有理论意义，为我国驾驶人行为谱的研究提供了一个科学、定量化分析的工具，对交管部门规范驾驶人驾驶行为、提高道路安全指数、制定合理的交通安全管理策略具有指导意义。