Popular scientific abstract
The development of transportation has greatly benefited our lives, however, traffic crashes can be also a threat to people’s life. Many safety systems have been developed to prevent traffic crashes and mitigate crash injuries. Plenty of driving data for manually driven cars have been collected and implemented in virtual simulation for safety benefit studies. Safety assessments of manually driven cars with and without safety systems have also been studied during past years. However, even though automated vehicles will be the future trend, there is no established safety assessment method available for automated vehicles interactions with pedestrians and bicyclists. There is limited automated vehicle driving data available and real-life experiment of automated vehicles can also face many difficulties. Automated vehicles can potentially improve (e.g., on-line software updates) or learn themselves from previous driving experience, which leads to more uncertainties in data collection. In addition to the lack of established safety assessment method, there is also a lack of robust statistical methods to use in conjunction with virtual assessment of safety. This project will address these two gaps and when this project come to the end, we will have better understanding of the influence of pedestrians and bicyclists interactions with automated vehicles, regarding the crash risks.
Jonas Bärgman: firstname.lastname@example.org
András Bálint (co-supervisor): email@example.com
As automated vehicles are to be introduced in traffic there is a clear and present need to evaluate the automated vehicles functionalities before the systems are running in production vehicles on our roads. To do this timely (e.g., during development or between software releases), virtual safety assessment is needed. Virtual simulations for safety benefit assessment has been increasingly popular and broadly implemented in last few decades (Lindman & Tivesten, 2006; Sander, 2017). Although there is ongoing research on methods for virtual assessment of traffic safety between automated cars and other road-users (Wang et al., 2020), there are several research gaps. The following are the two research gaps which this PhD will address: a) a lack of methods for virtually assessing the safety of automated vehicle interaction with pedestrians and bicyclists, b) a lack of robust statistical methods to use in conjunction with virtual assessment of safety, and that can use prior information and merge data from multiple data sources. For the former, continuation of current methods, but integrating models of pedestrian and bicyclist behavior models, is a next natural research step. For the latter, the use of Bayesian statistics as a framework to include prior information has been identified as a promising direction of research, but with very little prior work available.
Aims and objectives
The aim of this PhD project is a) to train the (Marie Curie) PhD on a wide variety of methods and theories within the field of human factors for vehicle automation in the future, with exposure also to industry, and b) to develop a method for virtually assessing the safety benefits of automated vehicles interaction with vulnerable road users (VRU; bicyclists and pedestrians) in an appropriate statistical framework, for example Bayesian, integrating computational models of human-interaction with automated vehicles into virtual counterfactual simulations as input to the framework.
- Develop and improve methods for the virtual assessment of the crash risks associated with the combination of the AV driver, the AV, and the VRUs interacting with it
- Integrate models of AV/VRU interactions into a virtual safety assessment
- Compare the safety (crash risk) of AVs and manually driven cars across different scenarios involving VRUs, taking into account VRU behavior variability
Together with 14 other ESRs, we will be trained and performing research on human factors aspects of the interaction between automated vehicles and humans in the cities of tomorrow in the Marie Curie project SHAPE-IT. Specifically, I will be performing research to develop and evaluate methods for virtually assessing the safety benefit of automated vehicles when interacting with vulnerable road users (VRU; pedestrians and bicyclists). The project has two main parts. First, the work includes the development of a simulation framework (concept and implementation of counterfactual simulations) to assess different car/VRU conflict situations. Second, the other main part of the work is to develop a statistical/methodological framework, for example using Bayesian statistics that uses the virtual simulations and prior knowledge about system benefits (and other aspects of the conflicts). The work will include the comparison of such a Bayesian framework and traditional statistical methods
- Extension of current virtual simulation methods, using a Bayesian framework to virtually evaluate how different AV and VRU behaviors affect crash risk
- Integration of interaction models between AVs and pedestrians and bicyclists in virtual safety assessment, collaborating with ESR13 and ESR14, respectively
- Better understanding of how different AV and VRU behaviors interact to affect (simulated) crash risk
References and links
Lindman, M., & Tivesten, E. (2006). A method for estimating the benefit of autonomous braking systems using traffic accident data. SAE Technical Papers, 2006(724). https://doi.org/10.4271/2006-01-0473
Sander, U. (2017). Opportunities and limitations for intersection collision intervention—A study of real world ‘left turn across path’ accidents. Accident Analysis and Prevention. https://doi.org/10.1016/j.aap.2016.12.011
Wang, L., Zhong, H., Ma, W., Abdel-Aty, M., & Park, J. (2020). How many crashes can connected vehicle and automated vehicle technologies prevent: A meta-analysis. Accident Analysis and Prevention. https://doi.org/10.1016/j.aap.2019.105299