Week 5 Lecture - Part 1 PDF

CVEN4405 Human Factors in Civil and Transport Engineering Week 5 lecture - Part 1 Micro-mobility, vulnerable road users, and philosophical aspects of road safety Dr Milad Haghani School of Civil and Environmental Engineering Week 5: Micro-mobility, vulnerable road users, & philosophical aspects o...

CVEN4405 Human Factors in Civil and Transport Engineering Week 5 lecture - Part 1 Micro-mobility, vulnerable road users, and philosophical aspects of road safety Dr Milad Haghani School of Civil and Environmental Engineering Week 5: Micro-mobility, vulnerable road users, & philosophical aspects of road safety • • • • • • • Pedestrian safety Key risk factors in pedestrian safety Pedestrian distraction VR & simulator applications in pedestrian research Field methods in pedestrian research Pedestrian interactions with Autonomous vehicles Moral dilemma in driving automation Pedestrian safety • More than half of road deaths are among vulnerable road users. • Vulnerable road users refer to individuals who are at a higher risk of injury or harm when using roadways due to their limited protection in the event of a collision. ➢ ➢ ➢ ➢ ➢ Pedestrians Cyclists Motorcyclists Electric scooter riders skateboarder riders • It is important for city officials to plan, design, and construct pathways that guarantee not only comfort and convenience, but also safety. • And, equally important, a safe walking environment will promote, motivate, and convince residents to participate actively in walking as an alternative to using private vehicles. Pedestrian safety Pedestrian safety • Pedestrian safety vs pedestrian security: The term pedestrian safety should not be confused with pedestrian security. Though both safety and security have adverse consequences to the pedestrians, they are not synonymous and are not interchangeable (Shah M.Z. et al, 2021). • Pedestrian safety: refers to situations when there are potential risks of personal injuries due to civil activities (e.g., driving or using mobile phone while walking) or due to hazards from physical obstructions and natural environments. • Pedestrian security: refers to risks of personal injuries due to criminal activities such as snatch theft, street crime, kidnapping, sexual assault, and terrorism as in the case when motor vehicles are used as weapons of destruction to slam into pedestrians and cyclists. Pedestrian safety • Walking, just like driving or any other form of transportation, has its associated safety risks. For pedestrians, the safety risks are extremely high and serious as pedestrians are the most vulnerable among all road users due to the absence of any form of physical protection. • There are four factors that contribute to elevated risks to pedestrian safety—physical obstructions factors, vehicular factors, design factors, and human factors. • Road User Hierarchy: This concept establishes the priority level of every road user. • Adopting this hierarchy requires that city officials give guarantees, in their design, planning philosophies, and development processes, that pedestrians are given the top priority to the roadway use and all other road users must give priority to the convenience, comfort, and safety of pedestrians. Road user hierarchy Pedestrian Protection Zone Buffer zone as spatial separator. Local statistics for pedestrian trauma on Australian roads Pedestrian safety • Data also show that more than two-thirds of fatal pedestrian crashes occur at night. • Research has documented that drivers’ inability to see pedestrians at a safe distance is a major factor responsible for nighttime pedestrian crashes. • Data show that approximately half of pedestrians killed in traffic crashes had been consuming alcohol. It is interesting to note that the level of impairment of pedestrians in fatal crashes is quite high. • There is a significant increase in crash risk for pedestrians with a blood alcohol content (BAC) higher than 0.20. • High income countries (HIC) have reported reduction in pedestrian fatalities as compared to low- and middle-income countries (LMICs), however pedestrian trips have also reduced in these countries leading to concerns about the effectiveness of ‘known’ strategies. • LMICs face pedestrian exposure on high-speed roads. • Researches have studied the epidemiology of pedestrian crashes, pedestrian behaviour, pedestrian movements and pedestrian flows. The suggested strategies to reduce pedestrian crashes have ranged from controlling vehicular speeds to controlling pedestrian behaviour. • Speed control through active measures have been found to have the maximal benefit and education and training programs for altering pedestrian behaviour on the road the least benefits. https://www.youtube.com/watch?v=0-nthHT-J1k https://www.youtube.com/watch?v=Eb0g5DP3zcY Pedestrian safety https://www.who.int/violence_injury_prevention/road_safety_status/2018/en/ Pedestrian safety Pedestrian safety The Pedestrian Behaviour Scale (PBS) is a self-report questionnaire that distinguishes five dimensions of pedestrian behaviour: • Violations • Errors • Lapses • Aggressive behaviours • Positive behaviours (The original version is in French) Week 5: Micro-mobility, vulnerable road users, & philosophical aspects of road safety • • • • • • • Pedestrian safety Key risk factors in pedestrian safety Pedestrian distraction VR & simulator applications in pedestrian research Field methods in pedestrian research Pedestrian interactions with Autonomous vehicles Moral dilemma in driving automation Pedestrian safety: Key risk factors • Speed: Increased vehicle speeds are associated with increased injury severity and death for pedestrians and cyclists. • Drink walking: Like drivers, a pedestrian’s risk of crash involvement increases with increasing blood alcohol content (BAC) due to the resulting impairment in judgement, reaction time, vigilance and visual acuity. There is currently no legal limit for pedestrians in Australia. • Distraction with technology: Due to the widespread use of smartphones, distraction by technology is an emerging risk factor for pedestrians, especially among adolescents and young adults. Compared to pedestrians who are not distracted, those using their mobile phones are found to walk more slowly, change direction more frequently, acknowledge other road users less, spend more time looking away from the traffic, and be less likely to check for traffic before crossing. • Inadequate visibility: Inadequate roadway lighting, vehicles and bikes without lights • Lack of pedestrian facilities in roadway design and land-use planning. https://www.youtube.com/watch?v=aHkVZ-4n_DY Pedestrian safety: Key risk factors • Fourteen experimental studies were ultimately included in an N-weighted meta-analysis (k=81 effect sizes), and eight observational studies were included in a qualitative overview. • Both mobile phone conversation and text messaging increased rates of hits and close calls. • Texting decreased rates of looking left and right prior to and/or during street crossing • As might be expected, text messaging was generally found to have the most detrimental effect on multiple behavioural measures. Week 5: Micro-mobility, vulnerable road users, & philosophical aspects of road safety • • • • • • • Pedestrian safety Key risk factors in pedestrian safety Pedestrian distraction VR & simulator applications in pedestrian research Field methods in pedestrian research Pedestrian interactions with Autonomous vehicles Moral dilemma in driving automation Pedestrian distraction: Applications of pedestrian simulators and VR Pedestrian distraction: Applications of pedestrian simulators and VR Distracted walking Impact of smartphone distraction on pedestrians’ crossing behaviour Sobhani, A., Farooq, B., 2018. Impact of smartphone distraction on pedestrians’ crossing behaviour: An application of head-mounted immersive virtual reality. Transportation Research Part F: Traffic Psychology and Behaviour 58, 228-241. Simulated road crossing from the perspective of the pedestrian looking at traffic Head-Mounted Immersive Virtual Reality (VIRE) and Respondent in VIRE The road street crossing structure The University of Sydney Page 18 Distracted walking Experiment design Head-Mounted Immersive Virtual Reality (VIRE) to simulate road crossing Three road crossing conditions: 1) Control condition: crossing the road with no distraction 2) Distracted condition: solving a maze-puzzle on a smartphone 3) Distracted condition with implemented safety measure: crossing the road while solving a maze-puzzle on a smartphone with implemented smart LED lights. The University of Sydney Page 19 Distracted walking Measurements Four types of variables were generated: 1) Crossing variables: crossing duration, wait time duration, crossing speed 2) Distraction attributes: percentage of the time the head was facing the smartphone during wait time/crossing 3) Socio-demographic information: age, gender 4) Safety measures: Post encroachment time* (dependant variable) *The time difference between when the pedestrian departs the collision point and the vehicle arrives at that point Laureshyn, Aliaksei, Åse Svensson, and Christer Hydén. "Evaluation of traffic safety, based on micro-level behavioural data: Theoretical framework and first implementation." Accident Analysis & Prevention 42, no. 6 (2010): 1637-1646. The University of Sydney Page 20 Distracted walking Outcomes The University of Sydney Page 21 Pedestrian distraction: Applications of pedestrian simulators and VR • • • • • How characteristics of the environment affect pedestrians’ road crossing behavior Effect of typical urban visual clutter created by objects and elements in the road proximity (e.g., billboards) on adults and children (aged 9–13) road crossing behavior Divided into three levels of visual load, results showed that high visual load affected children’s and adults’ road crossing behavior and visual attention. The main effect on participants’ crossing decisions was seen in missed crossing opportunities. Children, 9–10 and 11–13 years old, had a wider spread of gazes across the scene when the environment was highly loaded—an effect not seen with adults. Pedestrian distraction: Applications of pedestrian simulators and VR • • • • • • • Pedestrians in a one-lane road with continuous traffic using an immersive CAVE-based simulator. Sixty participants were recruited to complete a crossing task and perform one of two distractions, a visual-manual task and an auditory cognitive task. Normal and time pressure crossing conditions were included as a baseline and comparison. Although pedestrian safety was compromised under both types of distraction, the effects of the distractions were different. When engaged in the visual-manual distraction, participants crossed the road slowly, but there was no significant difference in gap acceptance or initiation time compared to baseline. Participants walked slowly, crossed earlier, and accepted smaller gaps when performing the auditory cognitive distraction. Compared to baseline, time pressure resulted in participants accepting smaller time gaps with shorter initiation times and crossing durations, leading to an increase in unsafe decisions. Week 5: Micro-mobility, vulnerable road users, & philosophical aspects of road safety • • • • • • • Pedestrian safety Key risk factors in pedestrian safety Pedestrian distraction VR & simulator applications in pedestrian research Field methods in pedestrian research Pedestrian interactions with Autonomous vehicles Moral dilemma in driving automation Pedestrian distraction: Field methods • • • • • Current signage at intersections is designed for attentive pedestrians who are looking ahead. Such signage may not be sufficient when distracted by smartphones. Illuminated in-ground LED lights at crossings are an innovative solution to alert distracted pedestrians A field study at a railway crossing equipped with in-ground lights to assess whether distracted pedestrians could detect these lights and how this impacted on their visual scanning and crossing behaviour. A distractor task presented through a mobile device (none, visual, and audio) on eye movements recorded using an eye tracker, and verbal reporting of when participants detected the lights. Participants were significantly less likely to check for trains when visually distracted (70%), a 10% reduction compared to the no or audio distractor conditions (80% and 78% respectively). The introduction of the in-ground lights resulted in appropriate scanning of the rail tracks (77% and 78% for the visual and auditory distractor tasks respectively) similar to that of non-distracted participants for the crossing without lights (80%). Week 5: Micro-mobility, vulnerable road users, & philosophical aspects of road safety • • • • • • • Pedestrian safety Key risk factors in pedestrian safety Pedestrian distraction VR & simulator applications in pedestrian research Field methods in pedestrian research Pedestrian interactions with Autonomous vehicles Moral dilemma in driving automation Interaction of pedestrians with AVs • • • • • • When autonomous vehicles are introduced, pedestrians and cyclists will still be present and would need to interact with these automated vehicles How the physical appearance of the AV and a mounted external human-machine interface (eHMI) affect pedestrians’ crossing intention. Crossing intentions were recorded, as well as their trust in automation and perceived behavioral control. The presence of a zebra crossing and larger gap size between the pedestrian and the vehicle increase the pedestrian’s intention to cross. Despite that the vehicle type affected the perceived risk of the participants, no significant difference was found in crossing intention. Participants who recognised the vehicle as an AV had, overall, lower intentions to cross. Interaction of pedestrians with AVs • • • Pedestrian behaviour during road crossing in the presence of approaching autonomous vehicles in more realistic virtual reality (VR) environments The autonomous vehicles are controlled using game theory Participants’ trajectories reveal a more cautious crossing behaviour in VR than in previous laboratory experiments. Week 5: Micro-mobility, vulnerable road users, & philosophical aspects of road safety • • • • • • • Pedestrian safety Key risk factors in pedestrian safety Pedestrian distraction VR & simulator applications in pedestrian research Field methods in pedestrian research Pedestrian interactions with Autonomous vehicles Moral dilemma in driving automation Moral dilemma of autonomous vehicles • How should self-driving cars make decisions when human lives hang in the balance? • The large-scale adoption of autonomous vehicles raises ethical challenges because autonomous vehicles may sometimes have to decide between killing one person or another. • Autonomous vehicles (AVs) should reduce traffic accidents, but they will sometimes have to choose between two evils, such as running over pedestrians or sacrificing themselves and their passenger to save the pedestrians. • With the introduction of AVs, not all crashes will be avoided, and some crashes will require AVs to make difficult ethical decisions in cases that involve unavoidable harm. • For example, the AV may avoid harming several pedestrians by swerving and sacrificing a passerby, or the AV may be faced with the choice of sacrificing its own passenger to save one or more pedestrians. • Although these scenarios appear unlikely, even low-probability events are bound to occur with millions of AVs on the road. • The algorithms that control AVs will need to embed moral principles guiding their decisions in situations of unavoidable harm. • To align moral algorithms with human values, we must start a collective discussion about the ethics of AVs. • Defining the algorithms that will help AVs make these moral decisions is a formidable challenge. Moral dilemma of autonomous vehicles • Participants in six Amazon Mechanical Turk studies approved of utilitarian AVs (i.e., AVs that sacrifice their passengers for the greater good) and would like others to buy them. • But they would themselves prefer to ride in AVs that protect their passengers at all costs. • The study participants disapprove of enforcing utilitarian regulations for AVs and would be less willing to buy such an AV. Participants (American respondents) strongly agreed that it would be more moral for AVs to sacrifice their own passengers when this sacrifice would save a greater number of lives overall. However, participants were less certain that AVs would be programmed in a utilitarian manner. -Utilitarian moral doctrine (consequentialism): the moral course of action is to minimise casualties. -Deontological perspective: asserts that certain actions – like killing an innocent person – are just wrong, even if they have good consequences. Moral dilemma of autonomous vehicles • In study two (n = 451 participants), participants were presented with dilemmas that varied the number of pedestrians’ lives that could be saved, from 1 to 100. • Imagining that a family member was in the AV negatively affected the morality of the sacrifice, as compared with imagining oneself alone in the AV. Even though participants still agreed that utilitarian AVs were the most moral, they preferred the self-protective model for themselves. Moral dilemma of autonomous vehicles • What would happen if people indicated their ethical preferences in a revised paradigm, one that allowed AVs to treat different humans equally? • People overwhelmingly selected the equality when it was available, revealing that they want autonomous vehicles to treat people equally. • It may be difficult to program a deep sense of egalitarianism* into machines, but autonomous vehicles can functionally value human lives equally by simply ignoring (or failing to detect) features such as gender, age and social class. People’s choices for how autonomous vehicles should be programmed to act in situations where human lives are at stake The equality allowed condition was similar to the forced inequality condition, but with the addition of a third option, (3) treat the lives of groups A and B equally (for example, treat the lives of children and elderly people equally). *Egalitarian doctrines: Generally characterised by the idea that all humans are equal in fundamental worth or moral status. Moral dilemma of autonomous vehicles Moral dilemma of autonomous vehicles • Experiment gathered 40 million decisions in ten languages from millions of people in 233 countries and territories. • Three strong preferences: the preference for sparing human lives, the preference for sparing more lives, and the preference for sparing young lives. • Some preferences based on gender or social status vary considerably across countries and appear to reflect underlying societallevel preferences for egalitarianism. For example, countries belonging to the Southern cluster show a strong preference for sparing females compared to countries in other clusters. An autonomous vehicle experiences a sudden brake failure. Staying on course would result in the death of two elderly men and an elderly woman who are crossing on a ‘do not cross’ signal (left). Swerving would result in the death of three passengers: an adult man, an adult woman, and a boy (right). In each row, ΔP is the difference between the probability of sparing characters possessing the attribute on the right, and the probability of sparing characters possessing the attribute on the left, aggregated over all other attributes. Relative advantage or penalty for each character, compared to an adult man or woman. For each character, ΔP is the difference the between the probability of sparing this character (when presented alone) and the probability of sparing one adult man or woman. For example, for the attribute age, the probability of sparing young characters is 0.49 greater than the probability of sparing older characters. For example, the probability of sparing a girl is 0.15 higher than the probability of sparing an adult man or woman. Moral dilemma of autonomous vehicles • In May 2016, the first deadly crash of a Tesla Autopilot car occurred, and the occupant of the car was killed. Tesla’s explanation: “Neither Autopilot nor the driver noticed the white side of the tractor- trailer against a brightly lit sky, so the brake was not applied”. • In March 2018, the first automated car crash that killed a pedestrian occurred. A pedestrian that was crossing the street went unnoticed by both the car and the back-up driver (Uber). • In the fatal Tesla and Uber crashes, both the machine driver and the human driver should have taken action and neither did. The mistakes of both the machine and the human led to the crash. • Both Tesla and Uber were exonerated from prosecution. Also, notably, press attention surrounding the Tesla incident was markedly skewed towards blaming the human driver for the crash. https://www.youtube.com/watch?v=6Kf3I_OyDlI https://www.youtube.com/watch?v=8ATJaVTpviQ https://www.youtube.com/watch?v=V2u3dcH2VGM https://www.youtube.com/watch?v=R8Up9Ph_a0Y https://www.youtube.com/watch?v=E531GxfEoB8 Moral dilemma of autonomous vehicles • When an automated car crashes and harms someone, how is blame and causal responsibility attributed to the human and machine drivers by people who hear about the crash? • Laws concerning principles of negligence currently adjudicate how responsibility and blame are assigned to the individuals who injure others in these harmful crashes. In a partially-automated system, however, blame and responsibility may be shared between a human and machine driver. • Participants were asked to consider hypothetical cases in which a pedestrian was killed by a car operated under shared control of a primary and a secondary driver and to indicate how blame should be allocated. • Allowing the de facto standards for shared-control vehicles to be established in courts by the jury system could fail to properly regulate the safety of those vehicles. • Policy implications: manufacturers price products to reflect the liability they expect to incur from the sale of those products. If manufacturers cannot assess the scope of the liability they will incur from automated vehicles, that uncertainty will translate to substantially inflated prices of automated vehicles. • Uncertainty about the extent of corporate liability for automated vehicle crashes may be slowing down automated vehicle adoption. Moral dilemma of autonomous vehicles Human the primary driver and machine the secondary driver: human-machine Scenario 1: the main driver makes the correct choice and the secondary driver incorrectly intervenes (bad intervention) vs Machine the primary driver and human is the secondary driver: machine-human Scenario 2: the main driver makes an error and the secondary driver fails to intervene (missed intervention) Analogous scenarios involving a single human driver (a regular car) or a single machine driver (a fully automated car) as well as two hypothetical two driver cars (driven by two humans or two machines) Moral dilemma of autonomous vehicles the x-axis labelling of first driver refers to the main driver, while the last driver refers to the secondary driver in dual-driver cars and the sole driver in sole-driver cars. For bad intervention, only one agent has erred (the last driver). This agent (whether user or industry) is blamed more than the other agent. For missed intervention, in dual-driver cars (rows 2–7), both agents have erred. When human and machine are sharing control (within the dotted rectangle), blame ratings of Industry drop significantly regardless of the role of the machine. In Study 1, blame to Industry in S1-MH (m1 = 57.2) is significantly less than in S1-MM. When both drivers make errors in cases of human–machine shared-control vehicles, the blame attributed to the machine is reduced. Moral dilemma of autonomous vehicles (1) (2) (3) Selfish: Protects the lives of passenger(s) over any number of bystanders; Altruistic (Utilitarian): Minimises the number of casualties, even if this leads to death of passenger(s). Conservative: Abstains from interfering in such situations. Hypothesis 1. secret/self > public/self Whether participants are less likely to choose the selfish AV for themselves if the choice is indicated on the car and clearly visible Moral dilemma of autonomous vehicles (1) (2) (3) Selfish: Protects the lives of passenger(s) over any number of bystanders; Altruistic (Utilitarian): Minimizes the number of casualties, even if this leads to death of passenger(s) Conservative (Egalitarian?): Abstains from interfering in such situations. Hypothesis 2. H2: secret choices: child > self; Whether participants prefer the most selfish strategy when deciding for their child, less selfish strategy when deciding for themselves and the least selfish strategy when deciding for everybody. Moral dilemma of autonomous vehicles Other issues • Predetermined decisions over random decisions in all cases. • Whether it would be right to hand over the control to the driver at the last instant. • Who is the right person or organisation to decide the ethics of self-driving cars. • Not discriminate between humans based on age, gender, or other parameters. • What if the autonomous car is hacked by a cybercriminal and commanded to carry out an accident to implicate the driver? In such cases, who is responsible for the accident and loss of lives? Week 5: Micro-mobility, vulnerable road users, & philosophical aspects of road safety • • • • • • • Pedestrian safety Key risk factors in pedestrian safety Pedestrian distraction VR & simulator applications in pedestrian research Field methods in pedestrian research Pedestrian interactions with Autonomous vehicles Moral dilemma in driving automation