The need for public policies to promote healthier food consumption: A comment on Wansink and Chandon (2014)

Current approaches to addressing obesity have fallen short. This is largely due to the many environmental forces that undermine people's self-regulatory capacity to be personally responsible for their food choices. Novel insights from the social sciences are needed to inform voluntary, health-promoting actions by companies, institutions, and citizens as well as the design of public health policies. Voluntary interventions that rely on nudges should complement traditional public health strategies such as taxation and restriction of child-targeted marketing in schools. In this commentary, we discuss four food policy issues that would benefit from consumer psychology research: (a) the restriction of food marketing to children, (b) provision of nutrition information through food labels, (c) improving school food environments, and (d) placing limits on portion sizes. Identifying effective solutions for obesity will require approaches that integrate psychological, public health, and legal perspectives and methods.     

Factors contributing to hit-and-run crashes in China

Hit-and-run accidents, or those where the perpetrator leaves the crash scene without reporting the event, are a serious concern because they can delay the rescue of victims, thereby increasing the fatality rate and severity of injuries. However, only a few studies exist on the factors that influence hit-and-run behavior, particularly in developing countries. Using data collected from Guangdong Province in China, this study applies a logistic regression model to analyze factors associated with hit-and-run behavior in five categories: crash attributes and human, vehicle, road, and environmental factors. This study finds that the probability of hit-and-run behavior increases with accidents that involve pedestrians, occur in dark driving conditions, and are caused by drivers who are male, middle-aged, and without a valid driver’s license, extensive driving experience, or automobile insurance. Therefore, we recommend closer supervision and better public education for different groups of people about traffic laws and regulations.     

Traffic safety climate attitudes of road users in Germany

This paper validates traffic safety climate attitudes based on a representative sample of road users of all travel modes. We use the German version of the Traffic Climate Scale (TCS) which was applied in a large-scale road safety survey in 2010. A total of 1680 people were surveyed. The sample is representative for socio-demographic characteristics and travel mode choice in Germany. Factor analysis reveals a three-factor structure of traffic safety climate with the factor ‘External affective demands’ describing emotional engagement in traffic, the factor ‘Internal requirements’ representing individual skills and abilities to successfully participate in traffic, and the factor ‘Functionality’ describing requirements for a functional traffic system. The less emotionally demanding and the more functional traffic is perceived to be, the safer people feel in traffic. External affective demands are consistently related to the perception of others’ driving/riding style but not to one’s own, whereas internal requirements are consistently related to one’s own driving/riding style but not to the perception of others. There is no relation between traffic safety climate and accidents or near accidents. Contrary to our expectations, a positive traffic safety climate is associated with more secondary tasks while driving and traffic violations. Behavioural control beliefs may mediate the traffic climate–traffic behaviour relationship. The results are discussed with reference to attitude research and the theory of planned behaviour in particular.     

Introducing naturalistic cycling data: What factors influence bicyclists’ safety in the real world?

Presently, the collection and analysis of naturalistic data is the most credited method for understanding road user behavior and improving traffic safety. Such methodology was developed for motorized vehicles, such as cars and trucks, and is still largely applied to those vehicles. However, a reasonable question is whether bicycle safety can also benefit from the naturalistic methodology, once collection and analyses are properly ported from motorized vehicles to bicycles. This paper answers this question by showing that instrumented bicycles can also collect analogous naturalistic data. In addition, this paper shows how naturalistic cycling data from 16 bicyclists can be used to estimate risk while cycling. The results show that cycling near an intersection increased the risk of experiencing a critical event by four times, and by twelve times when the intersection presented some form of visual occlusion (e.g., buildings and hedges). Poor maintenance of the road increased the risk tenfold. Furthermore, the risk of experiencing a critical event was twice as large when at least one pedestrian or another bicyclist crossed the bicyclist’s trajectory. Finally, this study suggests the two most common scenarios for bicycle accidents, which result from different situations and thus require different countermeasures. The findings presented in this paper show that bicycle safety can benefit from the naturalistic methodology, which provides data able to guide development and evaluation of (intelligent) countermeasures to increase cycling safety.     

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.     

Two-step communication for the interaction between automated vehicles and pedestrians

An important challenge of automated vehicles (AV) will be the cooperative interaction with surrounding road users such as pedestrians. One solution to compensate for the missing driver-pedestrian interaction might be explicit communication via external human machine interfaces (eHMIs). Additionally, implicit communication such as a vehicle pitch motion might support AVs when interacting with pedestrians. While previous work explored various explicit and implicit communication cues, these concepts communicated the intention of the AV at one single time point. Currently, empirical findings on two-step communication lack so far. In this study, we empirically test the effect of a two-step AV communication that uses an implicit cue at a long distance and subsequently provides an implicit or explicit cues at a short distance. We compared its efficiency to single-step communication concepts providing implicit or explicit cues at the shorter distance only. To explore whether the right communication cue is used at the right distance, we analyzed pedestrians’ fixations while approaching an AV using an eye tracking device.We conducted a virtual reality study (N = 30) with AV communication concept that provided an active pitch motion or an eHMI and compared them with a two-step AV communication concept that provided an additional active pitch motion at a long distance when approaching the pedestrian. Furthermore, we recorded pedestrians’ fixation behavior while the AV approached.Consistently to previous work, single-step AV communication showed a beneficial effect on crossing behavior. Pedestrians initiated their crossing earlier while approaching an AV with an active pitch motion or an eHMI compared to the baseline condition. While active pitch motion reduced subjective safety feeling, eHMI increased it. However, the two-step communication concept did not further improve pedestrians’ crossing initiation times and their safety feeling. The pattern of fixation behavior differed as a function of AV distance. When the approaching AV was far away, pedestrians exclusively looked at the environment. During the approach, pedestrians gradually fixated the bumper and the hood and only then the windshield of the AV. Hence, it seems to be useful to present an AV intent communication at a certain distance from the pedestrian. These findings posit the importance of considering pedestrians’ fixation behavior when developing communication concepts between AVs and pedestrians.     

Transition patterns of driving style from a traditional driving environment to a connected vehicle environment: A case of an extra-long tunnel road

To provide a better understanding of individual driver’s driving style classification in a traditional and a CV environment, spatiotemporal characteristics of vehicle trajectories on a road tunnel were extracted through a driving simulator-based experiment. Speed, acceleration, and rate of acceleration changes are selected as clustering indexes. The dynamic time warping and k-means clustering were adopted to classify participants into different risk level groups. To assess the driver behavior benefits in a CV environment, an indicator BI (behavior indicator, BI) was defined based on the standard deviation of speed, the standard deviation of acceleration, and the standard deviation of the rate of acceleration change. Then, the index BI of each driver was calculated. Furthermore, this paper explored driving style classification, not in terms of traditional driving environment, but rather the transition patterns from a traditional driving environment to a CV environment. The results revealed that inside a long tunnel, 80 % of drivers benefited from a CV environment. Moreover, drivers might need training before using a CV system, especially female drivers who have low driving mileage. In addition, the results showed that the driving style of 69 % of the drivers’ transferred from a high risk-level to a low risk-level when driving in a CV environment. The study results can be expected to improve driving training education programs and also to provide a valuable reference for developing individual in-vehicle human-machine interface projects and other proactive safety countermeasures.     

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.