scholarly journals Potential Crash Rate Benchmarks for Automated Vehicles

2021 ◽  
Author(s):  
Noah J. Goodall
Keyword(s):  
Stated Preference ◽  
Average Rate ◽  
Crash Risk ◽  
Automated Vehicles ◽  
Automated Driving ◽  
Automated Vehicle ◽  
Crash Rates ◽  
Potential Safety ◽  
Transportation Modes ◽  
Driving Task

Most automobile manufacturers and several technology companies are testing automated vehicles on public roads. While automation of the driving task is expected to reduce crashes, there is no consensus regarding how safe an automated vehicle must be before it can be deployed. An automated vehicle should be at least as safe as the average driver, but national crash rates include drunk and distracted driving, meaning that an automated vehicle that crashes at the average rate is somewhere between drunk and sober. In this paper, automated vehicle safety benchmarks are explored from three perspectives. First, crash rates from naturalistic driving studies are used to determine the crash risk of the model (i.e., sober, rested, attentive, cautious) driver. Second, stated preference surveys in the literature are reviewed to estimate the public’s acceptable automated vehicle risk. Third, crash, injury, and fatality rates from other transportation modes are compared as baseline safety levels. A range of potential safety targets is presented as a guide for policymakers, regulators, and automated vehicle developers to assist in evaluating the safety of automated driving technologies for public use.

scholarly journals Potential Crash Rate Benchmarks for Automated Vehicles

2021 ◽  
pp. 036119812110098
Author(s):  
Noah J. Goodall
Keyword(s):  
Stated Preference ◽  
Average Rate ◽  
Crash Risk ◽  
Automated Vehicles ◽  
Automated Driving ◽  
The Public ◽  
Crash Rates ◽  
Potential Safety ◽  
Transportation Modes ◽  
Driving Task

Most automobile manufacturers and several technology companies are testing automated vehicles (AVs) on public roads. While automation of the driving task is expected to reduce crashes, there is no consensus as to how safe an AV must be before it can be deployed. An AV should be at least as safe as the average driver, but national crash rates include drunk and distracted driving, meaning that an AV that crashes at the average rate is somewhere between drunk and sober. In this paper, safety benchmarks for AVs are explored from three perspectives. First, crash rates from naturalistic driving studies are used to determine the crash risk of the model (i.e., sober, rested, attentive, cautious) driver. Second, stated preference surveys in the literature are reviewed to estimate the AV risk acceptable to the public. Third, crash, injury, and fatality rates from other transportation modes are compared as baseline safety levels. A range of potential safety targets is presented as a guide for policymakers, regulators, and AV developers to assist in evaluating the safety of automated driving technologies for public use.

scholarly journals A Methodology for Normalizing Safety Statistics of Partially Automated Vehicles

2021 ◽  
Author(s):  
Noah J. Goodall
Keyword(s):  
Reporting Standards ◽  
Driver Assistance ◽  
Automated Vehicles ◽  
Automated Driving ◽  
Driver Assistance Systems ◽  
Driving System ◽  
Automated Vehicle ◽  
Crash Rates ◽  
Road Crash ◽  
Assistance Systems

The safety of increasingly automated vehicles is of great concern to regulators, yet crash rates are generally reported by manufacturers with proprietary metrics. Without consistent definitions of crashes and exposure, comparing automated vehicle crash rates with baseline datasets becomes challenging. This study investigates the reported on-road crash rates of one manufacturer’s partially automated driving system. Their reported crash rates are adjusted based on roadway classification and driver demographics to allow for direct comparison with the manufacturer’s own advanced driver assistance systems. Recommendations for uniform crash reporting standards are provided.

scholarly journals Driver intervention performance assessment as a key aspect of L3–L4 automated vehicles deployment

2021 ◽  
Vol 11 (1) ◽  
pp. 845-852
Author(s):  
Aleksandra Rodak ◽  
Paweł Budziszewski ◽  
Małgorzata Pędzierska ◽  
Mikołaj Kruszewski
Keyword(s):  
Performance Assessment ◽  
Minimal Risk ◽  
Automated Vehicles ◽  
Automated Driving ◽  
Driving System ◽  
Driving Task ◽  
Driving Conditions

Abstract In L3–L4 vehicles, driving task is performed primarily by automated driving system (ADS). Automation mode permits to engage in non-driving-related tasks; however, it necessitates continuous vigilance and attention. Although the driver may be distracted, a request to intervene may suddenly occur, requiring immediate and appropriate response to driving conditions. To increase safety, automated vehicles should be equipped with a Driver Intervention Performance Assessment module (DIPA), ensuring that the driver is able to take the control of the vehicle and maintain it safely. Otherwise, ADS should regain control from the driver and perform a minimal risk manoeuvre. The paper explains the essence of DIPA, indicates possible measures, and describes a concept of DIPA framework being developed in the project.

scholarly journals A temporal analysis of safety drivers taking back control in public roadway automated driving trials

2019 ◽  
Vol 63 (1) ◽  
pp. 1532-1536
Author(s):  
Nicole M. Corcoran ◽  
Daniel V. McGehee ◽  
T. Zachary Noonan
Keyword(s):  
Autonomous Vehicle ◽  
Temporal Analysis ◽  
Automated Vehicles ◽  
Automated Driving ◽  
The Public ◽  
Environment Research ◽  
Operational Design ◽  
Take Back ◽  
Driving Task ◽  
Over Time

In 2019, industry is in the testing stages of level 4 SAE/NHTSA automated vehicles. While in testing, L4 vehicles require a safety driver to monitor the driving task at all times. These specially trained drivers must take back control if the vehicle doesn’t seem to be responding correctly to the ever-changing roadway and environment. Research suggests that monitoring the driving task can lead to a decrease in vigilance over time. Recently, Waymo publicly released takeover request and mileage data on its 2018 L4 autonomous vehicle takeover requests. From this data, which was represented in mileage, we created temporal metric which showed that there were typically 150-250 hours without a takeover request. From this we suggest that there may be a decrement in vigilance for Waymo safety drivers. While there are still many unknowns, we suggest Waymo release takeover requests in terms of time rather than mileage and provide more information on the operational design domains of these vehicles. Expanding the content of this publicly-released data could then give researchers and the public more understanding of the conditions under which safety drivers are functioning.

scholarly journals Investigating the Impacts of Road Traffic Conditions and Driver’s Characteristics on Automated Vehicle Takeover Time and Quality Using a Driving Simulator

2021 ◽  
Vol 2021 ◽  
pp. 1-13
Author(s):  
Jaehyun Jason So ◽  
Sungho Park ◽  
Jonghwa Kim ◽  
Jejin Park ◽  
Ilsoo Yun
Keyword(s):  
Driving Simulator ◽  
Road Traffic ◽  
Vehicle Control ◽  
Automated Vehicles ◽  
Automated Driving ◽  
Automated Vehicle ◽  
The Road ◽  
Traffic Conditions ◽  
Vehicle System ◽  
Driver’S Characteristics

This study investigates the impacts of road traffic conditions and driver’s characteristics on the takeover time in automated vehicles using a driving simulator. Automated vehicles are barely expected to maintain their fully automated driving capability at all times based on the current technologies, and the automated vehicle system transfers the vehicle control to a driver when the system can no longer be automatically operated. The takeover time is the duration from when the driver requested the vehicle control transition from the automated vehicle system to when the driver takes full control of the vehicle. This study assumes that the takeover time can vary according to the driver’s characteristics and the road traffic conditions; the assessment is undertaken with various participants having different characteristics in various traffic volume conditions and road geometry conditions. To this end, 25 km of the northbound road section between Osan Interchange and Dongtan Junction on Gyeongbu Expressway in Korea is modeled in the driving simulator; the experiment participants are asked to drive the vehicle and take a response following a certain triggering event in the virtual driving environment. The results showed that the level of service and road curvature do not affect the takeover time itself, but they significantly affect the stabilization time, that is, a duration for a driver to become stable and recover to a normal state. Furthermore, age affected the takeover time, indicating that aged drivers are likely to slowly respond to a certain takeover situation, compared to the younger drivers. With these findings, this study emphasizes the importance of having effective countermeasures and driver interface to monitor drivers in the automated vehicle system; therefore, an early and effective alarm system to alert drivers for the vehicle takeover can secure enough time for stable recovery to manual driving and ultimately to achieve safety during the takeover.

scholarly journals A Taxonomy of Strategies For Supporting Time-Sharing With Non-Driving Tasks in Automated Driving

2019 ◽  
Vol 63 (1) ◽  
pp. 2088-2092
Author(s):  
Dengbo He ◽  
Dina Kanaan ◽  
Birsen Donmez
Keyword(s):  
Driver Distraction ◽  
Mitigation Strategies ◽  
Future Research ◽  
Time Sharing ◽  
Automated Vehicles ◽  
Automated Driving ◽  
Negative Effects ◽  
Spare Capacity ◽  
Control Automation ◽  
Driving Task

Driver distraction is one of the leading causes of vehicle crashes. The introduction of higher levels of vehicle control automation is expected to alleviate the negative effects of distraction by delegating the driving task to automation, thus enabling drivers to engage in non-driving-related tasks more safely. However, before fully automated vehicles are realized, drivers are still expected to play a supervisory role and intervene with the driving task if necessary while potentially having more spare capacity for engaging in non-driving-related tasks. Traditional distraction mitigation perspectives need to be shifted for automated vehicles from mainly preventing the occurrence of non-driving-related tasks to dynamically coordinating time-sharing between driving and non-driving-related tasks. In this paper, we provide a revised and expanded taxonomy of driver distraction mitigation strategies, discuss how the different strategies can be used in an automated driving context, and propose directions for future research in supporting time-sharing in automated vehicles.

scholarly journals Modeling Cross-National Differences in Automated Vehicle Acceptance

2020 ◽  
Vol 12 (22) ◽  
pp. 9765
Author(s):  
Shelly Etzioni ◽  
Jamil Hamadneh ◽  
Arnór B. Elvarsson ◽  
Domokos Esztergár-Kiss ◽  
Milena Djukanovic ◽  
...  
Keyword(s):  
Mode Choice ◽  
Continuous Distribution ◽  
Preference Heterogeneity ◽  
Type Model ◽  
Model Parameters ◽  
Automated Vehicles ◽  
Automated Driving ◽  
Automated Vehicle ◽  
National Differences ◽  
Private Car

The technology that allows fully automated driving already exists and it may gradually enter the market over the forthcoming decades. Technology assimilation and automated vehicle acceptance in different countries is of high interest to many scholars, manufacturers, and policymakers worldwide. We model the mode choice between automated vehicles and conventional cars using a mixed multinomial logit heteroskedastic error component type model. Specifically, we capture preference heterogeneity assuming a continuous distribution across individuals. Different choice scenarios, based on respondents’ reported trip, were presented to respondents from six European countries: Cyprus, Hungary, Iceland, Montenegro, Slovenia, and the UK. We found that large reservations towards automated vehicles exist in all countries with 70% conventional private car choices, and 30% automated vehicles choices. We found that men, under the age of 60, with a high income who currently use private car, are more likely to be early adopters of automated vehicles. We found significant differences in automated vehicles acceptance in different countries. Individuals from Slovenia and Cyprus show higher automated vehicles acceptance while individuals from wealthier countries, UK, and Iceland, show more reservations towards them. Nontrading mode choice behaviors, value of travel time, and differences in model parameters among the different countries are discussed.

Driver Vigilance in Automated Vehicles: Hazard Detection Failures Are a Matter of Time

2018 ◽  
Vol 60 (4) ◽  
pp. 465-476 ◽  
Author(s):  
Eric T. Greenlee ◽  
Patricia R. DeLucia ◽  
David C. Newton
Keyword(s):  
Safety Concern ◽  
Reaction Times ◽  
Vigilance Task ◽  
Time On Task ◽  
Vigilance Decrement ◽  
Automated Vehicles ◽  
Automated Driving ◽  
Automated Vehicle ◽  
Hazard Detection ◽  
And Performance

Objective: The primary aim of the current study was to determine whether monitoring the roadway for hazards during automated driving results in a vigilance decrement. Background: Although automated vehicles are relatively novel, the nature of human-automation interaction within them has the classic hallmarks of a vigilance task. Drivers must maintain attention for prolonged periods of time to detect and respond to rare and unpredictable events, for example, roadway hazards that automation may be ill equipped to detect. Given the similarity with traditional vigilance tasks, we predicted that drivers of a simulated automated vehicle would demonstrate a vigilance decrement in hazard detection performance. Method: Participants “drove” a simulated automated vehicle for 40 minutes. During that time, their task was to monitor the roadway for roadway hazards. Results: As predicted, hazard detection rate declined precipitously, and reaction times slowed as the drive progressed. Further, subjective ratings of workload and task-related stress indicated that sustained monitoring is demanding and distressing and it is a challenge to maintain task engagement. Conclusion: Monitoring the roadway for potential hazards during automated driving results in workload, stress, and performance decrements similar to those observed in traditional vigilance tasks. Application: To the degree that vigilance is required of automated vehicle drivers, performance errors and associated safety risks are likely to occur as a function of time on task. Vigilance should be a focal safety concern in the development of vehicle automation.

scholarly journals Travel experience matters: Expected personal mobility impacts after simulated L3/L4 automated driving

2021 ◽  
Author(s):  
Esko Lehtonen ◽  
Johanna Wörle ◽  
Fanny Malin ◽  
Barbara Metz ◽  
Satu Innamaa
Keyword(s):  
Driving Simulator ◽  
Stated Preference ◽  
Travel Times ◽  
Automated Vehicles ◽  
Automated Driving ◽  
Personal Mobility ◽  
Level Of Automation ◽  
Travel Experience ◽  
Gradual Development ◽  
Final Drive

AbstractAutomated vehicles (AVs) are expected to change personal mobility in the near future. Most studies on the mobility impacts of AVs focus on fully automated (SAE L5) vehicles, but the gradual development of the technology will probably bring AVs with more limited capabilities to begin with. This stated-preference study focused on the potential mobility impacts of conditionally automated (L3) and highly automated cars (L4). We investigated personal mobility impacts among 59 participants who experienced automated driving repeatedly in a driving simulator. Half of them drove with an L3 and half with an L4 motorway function. After the first and final drive they answered questions on their travel experience and how automated vehicles could change their mobility. After the drives, participants in both groups were willing to accept 30–50% longer travel times for a 30 min trip if they did not need to drive the whole trip themselves. This translates into savings of around 30% for the perceived value of travel time on routes where automation is available. There were no statistically significant differences between L3 and L4 in the accepted travel times. Most participants did not expect to make more trips with automated cars, but around half of them anticipated making longer trips. The amount of car travel may increase more with L4 than with L3 automation, possibly due somewhat to changes in the experienced travel quality. The results suggest that the mobility impacts of automated driving may increase with a higher level of automation.

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