A focus group study on the potential of autonomous vehicles as a viable transportation option: Perspectives from people with disabilities and public transit agencies

Autonomous vehicle (AV) technology is becoming one of the most promising alternatives to improve mobility for people with disabilities. Nevertheless, how people with disabilities perceive AV as transportation services has not been explored. Also, limited information exists about how public transit agencies comprehend and perceive autonomous vehicle transportation (AVT) services. This study discusses mobility issues for people with disabilities and explores the potential of AVT to serve that population, particularly those with visual impairments or physical disabilities. Researchers conducted six focus groups comprising people with disabilities (N = 23) and public transit service experts (N = 10) in Austin, Texas and Houston, Texas. Each session was audio-recorded and analyzed using conventional content analysis. This study identified people with disabilities’ mobility issues related to: (1) current transit services (including fixed-route and paratransit services) and (2) the quality of neighborhood built environments. Both people with disabilities and transit experts expected that AVT could mitigate current mobility issues, especially in improved built environments. However, participants with disabilities also expressed concerns and anxieties regarding AVT. Transit experts agreed that more targeted strategies would be needed to overcome possible barriers to AVT for people with disabilities. This study provides insights on shaping AVT strategies and policies relevant to improving mobility for people with disabilities.     

Road user interactions in a shared space setting: Priority and communication in a UK car park

Appropriate communication between road users can lead to safe and efficient interactions in mixed traffic. Understanding how road users communicate can support the development of effective communication methods for automated vehicles. We carried out observations of 66 pedestrian-driver and 124 driver-driver interactions in a shared space setting. Specific actions and reactions of the road users involved were recorded using a novel observation protocol. Overall, results showed that pedestrians’ failure to look towards a driver created the greatest uncertainty in the interaction, with the driver slowing down, but not completely stopping, in response to pedestrians. Looking towards the driver also influenced which road user took priority in driver-driver interactions. Groups of pedestrians were more likely to be given priority than an individual pedestrian, and the use of vehicle-based signals were also associated with taking priority during an interaction. Our observations show the importance of non-verbal communication during road user interactions, highlighting it as an essential area of research in the development of automated vehicles, to allow their safe, cooperative, interactions with other road users. Observations were made on a limited number of interactions to inform challenges facing future automated vehicles. Further work should therefore be done to corroborate and extend our findings, to examine interactions between human road users and automated vehicles in shared space settings.     

Psychometric properties of the Driving Behaviour Scale (DBS) among polish drivers

Anxiety can negatively affect an individual’s psychological wellbeing and lead to mild-to-moderate functional impairment in various areas of their lives. Despite this, the relationship between anxiety and driving performance has received very little empirical attention. The Driving Behaviour Scale (Clapp, Olsen, Beck, et al., 2011, Clapp, Olsen, Danoff-Burg, et al., 2011) was developed as a measure of anxious driving behaviours to support research in this area. The current study details adaptation and validation of the Driving Behaviour Scale (DBS; Clapp, Olsen, Beck, et al., 2011, Clapp, Olsen, Danoff-Burg, et al., 2011) in 310 university students in Poland. The overall internal consistency for the DBS was 0.76, while the two subscales demonstrated acceptable internal consistency (safety/cautious = 0.75 and hostile/aggressive behaviours = 0.85). The reliability estimates for performance deficit returned a lower coefficient of 0.65. Factor analysis produced a three-factor solution that supported the original structure of the DBS. The DBS may be utilised as a measure of driving anxiety in samples drawn from the general population.     

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.     

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.     

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.     

Handing control back to drivers: Exploring the effects of handover procedure during transitions from Highly Automated Driving

The operational capabilities of automated driving features are limited and sometimes require drivers’ intervention through a transition of control. Assistance at an operational level might be extremely beneficial during transitions but the literature lacks evidence on the topic. A simulator study was conducted to investigate the potential impacts that lateral assistance systems might have while the Automated Driving System (ADS) hands back control to the driver. Results showed that drivers benefitted from a strong Lane Keeping Assist during the first phase of the transfer, helping them to keep the lane centre. However, assisting the drivers at an operational level did not enhance their capability of addressing a more complex task, presented as a lane change. In fact, it was more task-specific assistance (Blind-spot assist) that allowed drivers to better cope with the tactical decision that the lane change required. Moreover, longer exposure to lane-keeping assist systems helped them in gaining awareness of the surrounding traffic and improved the way drivers interacted with the Blind-spot assist.     

Active vehicle pitch motion for communication in automated driving

The number of automated vehicles (AVs) is expected to successively increase in the near future. This development has a considerable impact on the informal communication between AVs and pedestrians. Informal communication with the driver will become obsolete during the interaction with AVs. Literature suggests that external human machine interfaces (eHMIs) might substitute the communication between drivers and pedestrians. In the study, we additionally test a recently discussed type of communication in terms of artificial vehicle motion, namely active pitch motion, as an informal communication cue for AVs.N = 54 participants approached AVs in a virtual inner-city traffic environment. We explored the effect of three communication concepts: an artificial vehicle motion, namely active pitch motion, eHMI and the combination of both. Moreover, vehicle types (sports car, limousine, SUV) were varied. A mixed-method approach was applied to investigate the participantś crossing behavior and subjective safety feeling. Furthermore, eye movement parameters were recorded as indicators for mental workload.The results revealed that any communication concept drove beneficial effects on the crossing behavior. The participants crossed the road earlier when an active pitch motion was present, as this was interpreted as a stronger braking. Further, the eHMI and a combination of eHMI and active pitch motion had a positive effect on the crossing behavior. The active pitch motion showed no effect on the subjective safety feeling, while eHMI and the combination enhanced the pedestrianś safety feeling while crossing. The use of communication resulted in less mental workload, as evidenced by eye-tracking parameters. Variations of vehicle types did not result in significant main effects but revealed interactions between parameters. The active pitch motion revealed no learning. In contrast, it took participants several trials for the eHMI and the combination condition to affect their crossing behavior. To sum up, this study indicates that communication between AVs and pedestrians can benefit from the consideration of vehicle motion.     

External communication of automated vehicles in mixed traffic: Addressing the right human interaction partner in multi-agent simulation

The urban traffic system is most likely to change in the next years to a mixed traffic with human drivers, vulnerable road users, and automated vehicles. In the past, the development of external communication approaches for automated vehicles focused on scenarios where an automated vehicle communicates with either a pedestrian or a human driver. However, interactions with more than one traffic partner are more realistic. Therefore, a study with 42 participants was conducted with a multi-agent simulation in which an automated vehicle interacted simultaneously with two participants, a pedestrian and a driver of a manual vehicle. In this study, two main scenarios were investigated in order to evaluate the safety and efficiency of the interactions and to determine whether the human road users feel correctly addressed. In one scenario, the pedestrian had to cross the road in front of the automated and the manual vehicle, which were approaching from different sides. In the other, the manual vehicle had to drive through a bottleneck in front of the oncoming automated vehicle, while the pedestrian had to cross the road after both vehicles passed. The communication approach of the automated vehicle consisted of implicit signals using a speed profile and lateral offset within its lane, and explicit signals using an external human–machine interface. The results of the study show that no collisions were observed in terms of safety and no significant negative effects on efficiency were measured. However, in contrast to single agent interactions, a majority of participants felt wrongly addressed in situations where the automated vehicle signals the right-of-way to the other human road user. It can be concluded that the communication approach of the automated vehicle needs to be modified in order to address certain road users more clearly.