The impact of navigation system display size and environmental illumination on young driver mental workload

Driver distraction is a major cause of road crashes and has a great influence on road safety. In vehicles, one of the common distracting sources is navigation systems (NSs). The navigation system (NS) can distract the driver due to following directions and reading the provided information through its display. These tasks take the driver’s attention from the primary task of driving and may cause poor driving performance, increasing the risk of crashes. In this paper, the effect of the environment (i.e., urban areas and rural areas), the navigation system display (NSD) size, environmental illumination, and gender on young drivers between the ages of 18 and 29 years mental workload was investigated using a simulated driving experiment. To evaluate each driving condition, the NASA-TLX (NASA Task Load Index) workload assessment tool, and a distraction evaluation element, were introduced and used to assess the overall workload, the workload subscales and the distraction by the NSD. The assessment showed a higher perceived overall workload for urban areas and night driving as compared to a rural areas and daytime driving. Moreover, the results showed a greater perceived distraction by the NSD in urban areas compared to driving in rural areas. The subjects also felt distracted when using the small NS compared to using the large NS. The study concluded that urban areas driving, and night driving creates higher perceived workload than rural areas and daytime driving. Furthermore, small NSD leads to more perceived distraction than large NSD while driving. The NSD designers may utilize this research findings to optimize NSD designs to improve driving safety, performance and comfort. Moreover, this study contributes to our understanding of the effect of the NSD size on driving workload and distraction.     

Effects of route familiarity on drivers’ psychological conditions: Based on driving behaviour and driving environment

Route familiarity affects a driver’s mental state and indirectly affects traffic safety; however, this important factor is easily overlooked. Previous research on route familiarity has only analysed psychological states in terms of unfamiliarity and familiarity, the influence of driving behaviour and driving environment on psychological states has been ignored. As a result, the mechanisms through which the route familiarity influence driver psychological states, and vice versa, are unclear. This study proposes a quantitative framework for studying driver psychological condition and route familiarity using experimental data from a real driving task and driving environment data. The experimental data included 1022 observations obtained by 23 participants over 7 consecutive trials on 6 unfamiliar experimental routes with large differences in scenarios; environmental data were automatically extracted after segmenting a driving video through the Dilated Residual NetWorks model. The results reveal that (1) the relationship between the driver’s psychological condition and route familiarity is not monotonic and is different for straight and turning sections; (2) the driver’s psychological condition is influenced by the visual scene elements and the type of road section, and the results of the multivariate regression analysis quantified the variability of the influence; and (3) unlike a majority of findings on distracted driving, our study suggest that the driver’s attention to the external environment in the urban distracted driving state will gradually approach a ‘distraction threshold’, and the time and size of the ‘distraction threshold’ are influenced by the driver. This study can further the development of urban traffic safety research and help urban designers plan and improve urban landscapes to ensure drivers maintain stable mental states when they drive.     

Oddity learning by African dwarf goats ( Capra hircus)

Seventeen African dwarf goats (adult females) were trained on oddity tasks using an automated learning device. One odd stimulus and three identical nonodd stimuli were presented on a screen divided into four sectors; the sector for the odd stimulus was varied pseudorandomly. Responses to the odd stimulus were deemed to be correct and were reinforced with food. In phase 1, the goats were trained on eight stimulus configurations. From trial to trial the odd discriminandum was either a + symbol or the letter S, and the nonodd discriminandum was the symbol not used as the odd one. In phase 2, the animals were similarly trained using an unfilled triangle or a filled (i.e., solid black) circle. In phase 3, three new discriminanda were used, an unfilled, small circle with radiating lines, an unfilled heart-shaped symbol, and an unfilled oval; which of the three discriminanda was odd and nonodd was varied from trial to trial. Following these training phases, a transfer test was given, which involved 24 new discriminanda sets. These were presented twice for a total of 48 transfer test trials. Results early in training showed approximately 25% correct, which might be expected by chance in a four-choice task. After 500-2,000 trials, results improved to approximately 40-44% correct. The best-performing subject reached 60-80% correct during training. On the transfer test, this subject had 47.9% correct and that significantly exceeded 25% expected by chance. This finding suggests that some exceptional individuals of African dwarf goats are capable of learning the oddity concept.     

自控摄入小剂量酒精影响熟练驾驶行为的实验研究

一个小样本受试者内设计实验,受试者在实验允许的酒精剂量范围内自由选择摄入量。实验安排饮酒前,酒后３０分钟、１１０分钟和１７０分钟四个测试阶段,分别检验受试者在模拟驾驶和实际驾驶两项任务中的认知行为。酒后３０分钟实际驾驶的技能与其他三个测试期相比存在有意义的差别显著性;模拟测试任务中酒后对红、黄两种信号灯的认知反应时之间呈现显著性差别,酒后１１０分钟对黄色信号灯的反应明显延迟。研究提示：受试对酒精感受性的估计是不可靠的,小剂量酒精也能对驾驶行为构成伤害。     

Do aggressive driving and negative emotional driving mediate the link between impulsiveness and risky driving among young Italian drivers?

ABSTRACT

The present study examined the contribution of impulsiveness and aggressive and negative emotional driving to the prediction of traffic violations and accidents taking into account potential mediation effects. Three hundred and four young drivers completed self-report measures assessing impulsiveness, aggressive and negative emotional driving, driving violations, and accidents. Structural equation modeling was used to assess the direct and indirect effects of impulsiveness on violations and accidents among young drivers through aggressive and negative emotional driving. Impulsiveness only indirectly influenced drivers’ violations on the road via both the behavioral and emotional states of the driver. On the contrary, impulsiveness was neither directly nor indirectly associated with traffic accidents. Therefore, impulsiveness modulates young drivers’ behavioral and emotional states while driving, which in turn influences risky driving.     

Semi-automated versus highly automated driving in critical situations caused by automation failures

The purpose of this study was to examine the effects of vehicle automation and automation failures on driving performance. Previous studies have revealed problems with driving performance in situations with automation failures and attributed this to drivers being out-of-the-loop. It was therefore hypothesized that driving performance is safer with lower than with higher levels of automation. Furthermore, it was hypothesized that driving performance would be affected by the extent of the automation failure. A moving base driving simulator was used. The design contained semi-automated and highly automated driving combined with complete, severe, and moderate deceleration failures. In total the study involved 36 participants. The results indicate that driving performance degrades when the level of automation increases. Furthermore, it is indicated that car drivers are worse at handling complete than partial deceleration failures.     

Moral disengagement mechanisms in interactions of human drivers with autonomous vehicles: Validation of a new scale and relevance with personality,driving style and attitudes

The introduction of autonomous vehicles (AVs) in the road transportation systems raises questions with respect to their interactions with human drivers’, especially during the early stages. Issues such as unfamiliarity or false assumptions regarding the timid and safe behaviour of AVs could potentially result in undesirable human driver behaviours, for instance “testing” AVs or being aggressive towards them. Among other factors, morality has been determined as a source of aggressive driving behaviour. Following previous approaches on moral disengagement, the current paper argues that moral standards during interactions of human drivers with AVs could potentially blur, leading to the disengagement of self-regulation mechanisms of moral behaviour. The study investigates the impact of moral disengagement on the intention of human drivers to be aggressive towards AVs. To that end, an online survey was conducted including a newly developed survey of moral disengagement, adapted to the context of AVs. Moreover, measures of personality, driving style, attitudes towards sharing the road with AVs and perceived threats were collected. A confirmatory factor analysis provided support for the concept of moral disengagement in the context of AVs. Moreover, relationships between personality, driving style and attitudes towards sharing the road with AVs were found, via a structural equation modelling approach (SEM). The results could have implications in the future driver training and education programmes, as it might be necessary to not only focus on driving skills but also on the development of procedural skills that will improve the understanding of AVs’ capabilities and ensure safer interactions. Efforts on improving attitudes towards AVs may also be necessary for improving human driver behaviour.     

On-road trust and perceived risk in Level 2 automation

To encourage appropriate use of driving automation, we need to understand and monitor driver’s trust and risk perception. We examined (1) how trust and perceived risk are affected by automation, driving conditions and experience and (2) how well perceived risk can be inferred from behaviour and physiology at three levels: over traffic conditions, aggregated risk events, and individual risk events.30 users with and without automation experience drove a Toyota Corolla with driving support. Safety attitude, subjective ratings, behaviour and physiology were examined.Driving support encouraged a positive safety attitude and active driver involvement. It reduced latent hazards while maintaining saliently perceived risks. Drivers frequently overruled lane centring (3.1 times/minute) and kept their feet on or above the pedals using ACC (65.8% of time). They comfortably used support on curvy motorways and monotonic and congested highways but less in unstable traffic and on roundabouts. They trusted the automation 65.4%, perceived 36.0% risk, acknowledged the need to monitor and would not engage in more secondary tasks than during manual driving.Trust-in situation reduced 2.0% when using automation. It was 8.2% higher than trust-in-automation, presumably due to driver self-confidence. Driving conditions or conflicts between driver and automation did not affect trust-in-automation.At the traffic condition level, physiology showed weak and partially counter-intuitive effects. For aggregated risk events, skin conductance had the clearest response but was discernible from baseline in  < 50%. Pupil dilation and heart rate only increased with strong braking and active lane departure assist. For individual risk events, a CNN classifier could not identify risk events from physiology. We conclude that GSR, heart rate and pupil dilation respond to perceived risk, but lack specificity to monitor it on individual events.     

Behavioural adaptation or constraint? The effect of curve proximity on driving behaviour

Horizontal curves are typically associated with increased crash risk when compared with straight roads, but recent analyses have suggested that having more frequent sharp curves decreases the relative crash risk posed by each curve. Here, 90 drivers completed a simulated rural drive with either high proximity (160 m straight tangent between curves) or low proximity (1200 m tangent) curves. Curve proximity had a significant effect on approach speeds, with drivers in the high proximity curve drive showing significantly lower mean and maximum approach speeds before entering the curve. However, they also showed an unexpected tendency to higher speeds while negotiating the curve itself. The current study provides direct empirical evidence that driving behaviour on approach to a given curve is significantly affected by the proximity of other curves, and therefore highlights the need to factor in the characteristics of the road on approach to the curve, as well as the features of the curve itself when assessing risk.     

Physiological indicators of driver workload during car-following scenarios and takeovers in highly automated driving

This driving simulator study, conducted as a part of Horizon2020-funded L3Pilot project, investigated how different car-following situations affected driver workload, within the context of vehicle automation. Electrocardiogram (ECG) and electrodermal activity (EDA)-based physiological metrics were used as objective indicators of workload, along with self-reported workload ratings. A total of 32 drivers were divided into two equal groups, based on whether they engaged in a non-driving related task (NDRT) during automation (SAE Level 3) or monitored the drive (SAE Level 2). Drivers in both groups were exposed to two counterbalanced experimental drives, lasting ∼ 18 min each, of Short (0.5 s) and Long (1.5 s) Time Headway conditions during automated car-following (ACF), which was followed by a takeover that happened with or without a lead vehicle. Results showed that driver workload due to the NDRT was significantly higher than both monitoring the drive during ACF and manual car-following (MCF). Furthermore, the results indicated that a lead vehicle maintain a shorter THW can significantly increase driver workload during takeover scenarios, potentially affecting driver safety. This warrants further research into understanding safe time headway thresholds to be maintained by automated vehicles, without placing additional cognitive or attentional demands on the driver. Our results indicated that ECG and EDA signals are sensitive to variations in workload, which warrants further investigation on the value of combining these two signals to assess driver workload in real-time, to help future driver monitoring systems respond appropriately to the limitations of the driver, and predict their performance in the driving task, if and when they have to resume manual control of the vehicle after a period of automated driving.