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。     

Speed of emotional information processing and emotional intelligence

The present study aimed to investigate the relationship between the speed of emotional information processing and emotional intelligence (EI). To evaluate individual differences in the speed of emotional information processing, a recognition memory task consisted of two subtests similar in design but differing in the emotionality of the stimuli. The first subtest required judgment about whether an emotional facial expression in the test face was identical to one of the four emotional expressions of the same individual previously presented. The second subtest required deciding whether the test face with a neutral emotional expression was identical to one of the four neutral faces of different individuals previously presented. Mean response latencies were calculated for “Yes” and “No” responses. All latencies were correlated with other measures of processing speed such as discrimination time and time of figure recognition. However, the emotional expression recognition subtest was hypothesized to require the processing of emotional information in addition to that of facial identity. Latencies in this subtest were longer than those in the face recognition subtest. To obtain a measure of the additional processing that was called for by the emotionality of the stimuli, a subtraction method and regression analysis were employed. In both cases, measures calculated for “No” responses were related to ability EI, as assessed via a self‐report questionnaire. According to structural equation modeling, there was a moderately negative association between latent EI and the latency of “No” responses in the subtest with emotional stimuli. These relationships were not observed for “Yes” responses in the same subtest or for responses in the subtest with neutral face stimuli. Although the differences between “Yes” and “No” responses in their associations with EI require further investigation, the results suggest that, in general, individuals with higher EI are also more efficient in the processing of emotional information.     

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.     

Does a sensory processing deficit explain counting accuracy on rapid visual sequencing tasks in adults with and without dyslexia?

The experiments conducted aimed to investigate whether reduced accuracy when counting stimuli presented in rapid temporal sequence in adults with dyslexia could be explained by a sensory processing deficit, a general slowing in processing speed or difficulties shifting attention between stimuli. To achieve these aims, the influence of the inter-stimulus interval (ISI), stimulus duration, and sequence length were evaluated in two experiments. In the first that used skilled readers only, significantly more errors were found with presentation of long sequences when the ISI or stimulus durations were short. Experiment 2 used a wider range of ISIs and stimulus durations. Compared to skilled readers, a group with dyslexia had reduced accuracy on two-stimulus sequences when the ISI was short, but not when the ISI was long. Although reduced accuracy was found on all short and long sequences by the group with dyslexia, when performance on two-stimulus sequences was used as an index of sensory processing efficiency and controlled, group differences were found with presentation of stimuli of short duration only. We concluded that continuous, repetitive stimulation to the same visual area can produce a capacity limitation on rapid counting tasks in all readers when the ISIs or stimulus durations are short. While reduced accuracy on rapid sequential counting tasks can be explained by a sensory processing deficit when the stimulus duration is long, slower processing speed in the group with dyslexia explains the greater inaccuracy found as sequence length is increased when the stimulus duration is short.     

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.