Investigation of older driver's takeover performance in highly automated vehicles in adverse weather conditions

The ability to continue driving into old age is strongly associated with older adults’ mobility and wellbeing for those that have been dependant on car use for most of their adult lives. The emergence of highly automated vehicles (HAVs) may have the potential to allow older adults to drive longer and safer. In HAVs, when operating in automated mode, drivers can be completely disengaged from driving, but occasionally they may be required to take back the control of the vehicle. The human-machine interfaces in HAVs play an important role in the safe and comfortable usage of HAVs. To date, only limited research has explored how to design age-friendly HMIs in HAVs and evaluate their effectiveness. This study designed three HMI concepts based on older drivers’ requirements, and conducted a driving simulator investigation with 76 drivers (39 older drivers and 37 younger drivers) to evaluate the effect and relative merits of these HMIs on drivers’ takeover performance, workload and attitudes. Results showed that the ‘R + V’ HMI (informing drivers of vehicle status together with providing the reasons for the manual driving takeover request) led to better takeover performance, lower perceived workload and highly positive attitudes, and is the most beneficial and effective HMI. In addition, The ‘V’ HMI (verbally informing the drivers about vehicle status, including automation mode and speed, before the manual driving takeover request) also had a positive effect on drivers’ takeover performance, perceived workload and attitudes. However, the ‘R’ HMI (solely informing drivers about the reasons for takeover as part of the takeover request) affected older and younger drivers differently, and resulted in deteriorations in performance and more risky takeover for both older and younger drivers compared to the baseline HMI. Moreover, significant age difference was observed in the takeover performance and perceived workload. Above all, this research highlights the significance of taking account older drivers’ requirements into the design of HAVs and the importance of collaboration between automated vehicle and cooperative ITS research communities.

ABSTRACTDriving is closely linked to older people’s mobility and independence. However, age-related functional decline reduces their safe driving abilities and thereby their wellbeing may decline. The rapid development of vehicle automation has the potential to enhance the mobility of older drivers by enabling them to continue driving safer for longer. So far only limited work has been carried out to study older drivers’ interaction with highly automated vehicles (HAV). This study investigates the effect of age and level of driving disengagement on the takeover control performance in HAV. A driving simulation study with 76 drivers has been conducted. Results showed that 20 s was sufficient for drivers to take over control from HAV. Older drivers take longer to respond and make decisions than younger drivers. The age effect on some aspects of takeover quality, in terms of operating steering wheel and pedals, is still pronounced. In addition, complete disengagement in driving in HAV leads to a longer takeover time and worse takeover quality, and it affects older drivers more seriously than younger drivers. The results highlight that an age-friendly design of human-machine interaction is important for enhancing the safety and comfort of older drivers when interacting with HAVs.

The population of older drivers is increasing in size. However, age-related functional decline potentially reduce their safe driving ability and thereby their wellbeing may decline. Fortunately, the forthcoming highly automated vehicles (HAVs) may have the potential to enhance the mobility of older drivers. HAVs would introduce a revolutionary human-machine interaction in which drivers can be completely disengaged from driving, and their control would be required occasionally. In order to inform the design of an age-friendly human-machine interaction in HAVs, several semi-structured interviews were conducted with 24 older drivers (mean = 71.50 years, SD = 5.93 years; 12 female, 12 male) to explore their opinions of and requirements towards HAV after they had hands-on experience with a HAV on a driving simulator. Results showed that older drivers were positive towards HAVs and welcomed the hands-on experience with HAVs. In addition, they wanted to retain physical and potential control over the HAVs, and would like to perform a range of non-driving related tasks in HAVs. Meanwhile, they required an information system and a monitoring system to support their interactions with HAVs. Moreover, they required the takeover request of HAVs to be adjustable, explanatory and hierarchical, and they would like the driving styles of HAVs to be imitative and corrective. Above all, this research provides recommendations to inform the design of age-friendly human-machine interactions in HAVs and highlights the importance of considering the older drivers’ requirements when designing and developing automated vehicles.

<div class="section abstract"><div class="htmlview paragraph">Studies of automatic emergency braking (AEB) find that AEB-equipped vehicles are around half as likely to crash. This study examines whether driver characteristics and road and weather conditions modify this preventive effect of AEB.</div><div class="htmlview paragraph">Toyota production data were merged with police reported crash files from eight U.S. states for crash years 2015 up to 2019 by 17-digit vehicle identification number (VIN). Using a case-control design, this study investigated the relationship of AEB presence with being a case vehicle in a system-relevant crash (the striking vehicle in front-to-rear crash; n=30,056) versus an AEB non-relevant control vehicle (the struck vehicle in a front-to-rear crash; n=62,820). The analysis was stratified by driver characteristics and by weather and road conditions. Logistic regression modeled the relationship, controlling for exposure (vehicle-days) and possible confounding factors. The resulting odds ratios for AEB equipment from the separate models were compared to determine if the effect of AEB presence was modified by the characteristic or condition of interest.</div><div class="htmlview paragraph">Overall, AEB-equipped vehicles were 43% (p<0.001) less likely to be the striking (case) vehicle compared to non-equipped vehicles. However, the preventive effect of AEB was significantly lower among older drivers (over 65 years) compared to younger drivers; 29% less likely to be a striking vehicle (OR=0.71) versus 46% (OR=0.54), respectively. The effect of AEB was also lower in adverse weather conditions (rain, fog, snow) (OR=0.66) and on wet or snowy roads (OR=0.65), though these differences were not significant compared to clear weather and dry roads. The AEB effect was also lower among risk-taking drivers (alcohol-involved, speeding, or unrestrained) compared to non-risk-taking (OR=0.72 versus OR=0.59, respectively).</div><div class="htmlview paragraph">AEB prevents crashes, regardless of driver characteristics and environmental conditions. This study suggests, however, that the size of the effect is smaller among older and risk-taking drivers, and in adverse weather and road conditions.</div></div>

Urban green spaces are an important part of the urban ecosystem and hold significant ecological value. To effectively protect these green spaces, urban managers urgently need to identify them and monitor their changes. Common urban vegetation positioning methods use deep learning segmentation models to process street view data in urban areas, but this is usually inefficient and inaccurate. The main reason is that they are not applicable to the variable climate of urban scenarios, especially performing poorly in adverse weather conditions such as heavy fog that are common in cities. Additionally, these algorithms also have performance limitations such as inaccurate boundary area positioning. To address these challenges, we propose the UGSAM method that utilizes the high-performance multimodal large language model, the Segment Anything Model (i.e., SAM). In the UGSAM, a dual-branch defogging network WRPM is incorporated, which consists of the dense fog network FFA-Net, the light fog network LS-UNet, and the feature fusion network FIM, achieving precise identification of vegetation areas in adverse urban weather conditions. Moreover, we have designed a micro-correction network SCP-Net suitable for specific urban scenarios to further improve the accuracy of urban vegetation positioning. The UGSAM was compared with three classic deep learning algorithms and the SAM. Experimental results show that under adverse weather conditions, the UGSAM performs best in OA (0.8615), mIoU (0.8490), recall (0.9345), and precision (0.9027), surpassing the baseline model FCN (OA improvement 28.19%) and PointNet++ (OA improvement 30.02%). Compared with the SAM, the UGSAM improves the segmentation accuracy by 16.29% under adverse weather conditions and by 1.03% under good weather conditions. This method is expected to play a key role in the analysis of urban green spaces under adverse weather conditions and provide innovative insights for urban development.

BackgroundMotor vehicle crashes are a leading cause of injury mortality. Adverse weather and road conditions have the potential to affect the likelihood of motor vehicle fatalities through several pathways. However, there remains a dearth of assessments associating adverse weather conditions to fatal crashes in the United States. We assessed trends in motor vehicle fatalities associated with adverse weather and present spatial variation in fatality rates by state.MethodsWe analyzed the Fatality Analysis Reporting System (FARS) datasets from 1994 to 2012 produced by the National Highway Traffic Safety Administration (NHTSA) that contains reported weather information for each fatal crash. For each year, we estimated the fatal crashes that were associated with adverse weather conditions. We stratified these fatalities by months to examine seasonal patterns. We calculated state-specific rates using annual vehicle miles traveled data for all fatalities and for those related to adverse weather to examine spatial variations in fatality rates. To investigate the role of adverse weather as an independent risk factor for fatal crashes, we calculated odds ratios for known risk factors (e.g., alcohol and drug use, no restraint use, poor driving records, poor light conditions, highway driving) to be reported along with adverse weather.ResultsTotal and adverse weather-related fatalities decreased over 1994–2012. Adverse weather-related fatalities constituted about 16 % of total fatalities on average over the study period. On average, 65 % of adverse weather-related fatalities happened between November and April, with rain/wet conditions more frequently reported than snow/icy conditions. The spatial distribution of fatalities associated with adverse weather by state was different than the distribution of total fatalities. Involvement of alcohol or drugs, no restraint use, and speeding were less likely to co-occur with fatalities during adverse weather conditions.ConclusionsWhile adverse weather is reported for a large number of motor vehicle fatalities for the US, the type of adverse weather and the rate of associated fatality vary geographically. These fatalities may be addressed and potentially prevented by modifying speed limits during inclement weather, improving road surfacing, ice and snow removal, and providing transit alternatives, but the impact of potential interventions requires further research.Electronic supplementary materialThe online version of this article (doi:10.1186/s12940-016-0189-x) contains supplementary material, which is available to authorized users.

The impact of weather on summer and winter exercise behaviors

This study explores generational differences in driving anxiety and trust in highly automated vehicles (HAVs) with level 3 automation to identify anxiety-inducing driving scenarios and inform age-specific empathic interface design. Twenty-one participants (14 young drivers, 7 older drivers) took part in focus groups discussing driving anxiety, trust in automation, and preferences for HAV design. Participants evaluated video clips of challenging driving scenarios and described their expectations for system behavior. Younger drivers frequently cited pedestrians and non-driving road users as sources of anxiety, while older drivers were more concerned with surrounding drivers and environmental conditions. Trust in automation was shaped by system transparency, technological performance, and perceived control. Older adults showed greater reluctance to cede control to HAV. These findings highlight the need for personalized, real-time feedback and trust-building features. Drawing on these findings, we developed design guidelines for age-specific empathic interfaces, which contribute to the design of user-centered HAV systems.

Identifying crash-prone traffic conditions under different weather on freeways

Objective Vehicle automation technologies have the potential to address the mobility needs of older adults. However, age-related cognitive declines may pose new challenges for older drivers when they are required to take back or “takeover” control of their automated vehicle. This study aims to explore the impact of age on takeover performance under partially automated driving conditions and the interaction effect between age and voluntary non-driving-related tasks (NDRTs) on takeover performance. Method A total of 42 older drivers (M = 65.5 years, SD = 4.4) and 40 younger drivers (M = 37.2 years, SD = 4.5) participated in this mixed-design driving simulation experiment (between subjects: age [older drivers vs. younger drivers] and NDRT engagement [road monitoring vs. voluntary NDRTs]; within subjects: hazardous event occurrence time [7.5th min vs. 38.5th min]). Results Older drivers exhibited poorer visual exploration performance (i.e., longer fixation point duration and smaller saccade amplitude), lower use of advanced driving assistance systems (ADAS; e.g., lower percentage of time adaptive cruise control activated [ACCA]) and poorer takeover performance (e.g., longer takeover time, larger maximum resulting acceleration, and larger standard deviation of lane position) compared to younger drivers. Furthermore, older drivers were less likely to experience driving drowsiness (e.g., lower percentage of time the eyes are fully closed and Karolinska Sleepiness Scale levels); however, this advantage did not compensate for the differences in takeover performance with younger drivers. Older drivers had lower NDRT engagement (i.e., lower percentage of fixation time on NDRTs), and NDRTs did not significantly affect their drowsiness but impaired takeover performance (e.g., higher collision rate, longer takeover time, and larger maximum resulting acceleration). Conclusions These findings indicate the necessity of addressing the impaired takeover performance due to cognitive decline in older drivers and discourage them from engaging in inappropriate NDRTs, thereby reducing their crash risk during automated driving.

<div class="section abstract"><div class="htmlview paragraph">Automated driving is an important development direction of the current automotive industry. Level 3 automated driving allows the driver to perform non-driving related tasks (NDRTs) during automated driving, however, once the operating conditions exceed the designed operating domain, the driver is still required to take over. Therefore, it is important to rationally design takeover requests (TORs) in Level 3 conditional automated driving. This paper investigates the effect of directional tactile guidance on driver takeover performance in emergency obstacle avoidance scenarios during the transfer of control from automated driving mode to manual driving. 18 participants drove a Level 3 conditional automated driving vehicle in a driving simulator on a two-way four-lane urban road, performed a takeover, and avoided obstacles while performing non-driving related tasks. The driver's takeover performance during the takeover process was measured and subjective driver evaluation data was collected via a questionnaire, which was subsequently statistically analyzed. The following conclusions were obtained: under the vibrotactile takeover request that provides away-from-danger direction information, the driver's takeover time is significantly lower than that of the vibrotactile takeover request that does not provide direction information; the vibrotactile takeover request that provides away-from-danger direction information is better than that of the vibrotactile takeover request that does not provide direction information in terms of the interaction experience and emotional experience, as well as the user experience, with a significant difference. This study provides a reference for the design of Level 3 automated driving takeover requests, significantly improving the operational safety of Level 3 automated driving vehicles.</div></div>

This study aimed at investigating the driver's takeover performance when switching from working on different non-driving related tasks (NDRTs) while driving with a conditionally automated driving function (SAE L3), which was simulated by a Wizard of Oz vehicle, to manual vehicle control under naturalistic driving conditions. Conditionally automated driving systems, which are currently close to market introduction, require the user to stay fallback ready. As users will be allowed to engage in more complex NDRTs during the automated drive than when driving manually, the time needed to regain full manual control could likely be increased. Thirty-four users engaged in different everyday NDRTs while driving automatically with a Wizard of Oz vehicle. After approximately either 5 min or 15 min of automated driving, users were requested to take back vehicle control in noncritical situations. The test drive took place in everyday traffic on German freeways in the metropolitan area of Stuttgart. Particularly tasks that required users to turn away from the central road scene or hold an object in their hands led to increased takeover times. Accordingly, increased variance in the driver's lane position was found shortly after the switch to manual control. However, the drivers rated the takeover situations to be mostly "harmless." Drivers managed to regain control over the vehicle safely, but they needed more time to prepare for the manual takeover when the NDRTs caused motoric workload. The timings found in the study can be used to design comfortable and safe takeover concepts for automated vehicles.

Is take-over time all that matters? The impact of visual-cognitive load on driver take-over quality after conditionally automated driving

ABSTRACTWyoming's Interstate 80 has one of the highest truck crash rates in the United States. This is due to a variety of reasons, including high percentage of truck traffic, adverse weather conditions and mountainous terrain. These factors have caused the Wyoming Highway Patrol (WHP) to spend extensive resources on inspecting commercial vehicles and enforcement of traffic laws in this corridor. This study estimated the correlation between traffic citations and truck crashes. In addition, the paper evaluated the increased risk of truck crashes in adverse weather and road conditions. The explanatory variables included geometric features, weather condition, traffic volume and types of citations. This research concluded that speeding related citations and truck crashes are negatively correlated, and the risk of truck crashes is significantly higher when weather is not clear, and the road is not dry.

Effects of weather and weather forecasts on driver behaviour