A review of surrogate safety measures and their applications in connected and automated vehicles safety modeling

Wang, Chen; Xie, Yuanchang; Huang, Helai; Liu, Pan

doi:10.1016/j.aap.2021.106157

Cited by 210 publications

(87 citation statements)

References 76 publications

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“…Even with the high cloud scalability and its low cost, solutions such as, increasing the computational power are not feasible in terms of data management challenges [11]. Moreover, safety applications will require thorough analysis in terms of safety evaluation and modeling [70][71][72]. In the following subsections, some of the most popular CV safety applications are discussed.…”

Section: Safety Applicationsmentioning

confidence: 99%

Connected Vehicles: Technology Review, State of the Art, Challenges and Opportunities

Abdelkader

Elgazzar

Khamis

2021

Sensors

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In an effort to reach accident-free milestones or drastically reduce/eliminate road fatalities rates and traffic congestion and to create disruptive, transformational mobility systems and services, different parties (e.g., automakers, universities, governments, and road traffic regulators) have collaborated to research, develop, and test connected vehicle (CV) technologies. CVs create new data-rich environments and are considered key enablers for many applications and services that will make our roads safer, less congested, and more eco-friendly. A deeper understanding of the CV technologies will pave the way to avoid setbacks and will help in developing more innovative applications and breakthroughs. In the CV paradigm, vehicles become smarter by communicating with nearby vehicles, connected infrastructure, and the surroundings. This connectivity will be substantial to support different features and systems, such as adaptive routing, real-time navigation, and slow and near real-time infrastructure. Further examples include environmental sensing, advanced driver-assistance systems, automated driving systems, mobility on demand, and mobility as a service. This article provides a comprehensive review on CV technologies including fundamental challenges, state-of-the-art enabling technologies, innovative applications, and potential opportunities that can benefit automakers, customers, and businesses. The current standardization efforts of the forefront enabling technologies, such as Wi-Fi 6 and 5G-cellular technologies are also reviewed. Different challenges in terms of cooperative computation, privacy/security, and over-the-air updates are discussed. Safety and non-safety applications are described and possible future opportunities that CV technology brings to our life are also highlighted.

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Section: Safety Applicationsmentioning

confidence: 99%

Connected Vehicles: Technology Review, State of the Art, Challenges and Opportunities

Abdelkader

Elgazzar

Khamis

2021

Sensors

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“…Time-To-Collision (TTC): It is the time required to observe a collision between an EV and an obstacle if both of them continue to travel without changing velocities (Minderhoud and Bovy, 2001;Vogel, 2003;Forkenbrock and Snyder, 2015;Johnsson et al, 2018;Li et al, 2021;Wang et al, 2021). It is one of the most popular safety indicators of longitudinal motion of the EV and is given by Hou et al (2014):…”

Section: Traditional Metricsmentioning

confidence: 99%

“…Traditional TTC (e.g., Minderhoud and Bovy (2001); Wang et al, 2021) • Time to an impending collision • Objective -TET (e.g., Mahmud et al (2017); Wang et al, 2021) • Duration for which gap maintained was lesser than a threshold Metrics used for performance evaluation of the environment perception module and the motion planning module vary between the ADS developers/operators. Appropriateness of performance metrics in the precrash scenario is a research question to be assessed.…”

Section: Metrics Description Advantages Drawbacksmentioning

confidence: 99%

“…Self-developed performance metrics are being employed to improve the ADS, using data from limited field deployments. Several other research studies related to the safety of ADS are already published (e.g., Alkhoury, 2017;Every et al, 2017;Fraade-blanar et al, 2018;Nistér et al, 2019;Wood et al, 2019;Berk et al, 2020;Riedmaier et al, 2020;Weng et al, 2020;Wishart et al, 2020;Bansal et al, 2021;Elli et al, 2021;Huang and Kurniawati, 2021;Luiten et al, 2021;Wang et al, 2021). Researchers across the globe are making a considerable effort to quantify the safety of ADS and consequently improve it.…”

Section: Introductionmentioning

confidence: 99%

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A Literature Review of Performance Metrics of Automated Driving Systems for On-Road Vehicles

Sharath

Mehran

2021

Front. Future Transp.

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The article presents a review of recent literature on the performance metrics of Automated Driving Systems (ADS). More specifically, performance indicators of environment perception and motion planning modules are reviewed as they are the most complicated ADS modules. The need for the incorporation of the level of threat an obstacle poses in the performance metrics is described. A methodology to quantify the level of threat of an obstacle is presented in this regard. The approach involves simultaneously considering multiple stimulus parameters (that elicit responses from drivers), thereby not ignoring multivariate interactions. Human-likeness of ADS is a desirable characteristic as ADS share road infrastructure with humans. The described method can be used to develop human-like perception and motion planning modules of ADS. In this regard, performance metrics capable of quantifying human-likeness of ADS are also presented. A comparison of different performance metrics is then summarized. ADS operators have an obligation to report any incident (crash/disengagement) to safety regulating authorities. However, precrash events/states are not being reported. The need for the collection of the precrash scenario is described. A desirable modification to the data reporting/collecting is suggested as a framework. The framework describes the precrash sequences to be reported along with the possible ways of utilizing such a valuable dataset (by the safety regulating authorities) to comprehensively assess (and consequently improve) the safety of ADS. The framework proposes to collect and maintain a repository of precrash sequences. Such a repository can be used to 1) comprehensively learn and model the precrash scenarios, 2) learn the characteristics of precrash scenarios and eventually anticipate them, 3) assess the appropriateness of the different performance metrics in precrash scenarios, 4) synthesize a diverse dataset of precrash scenarios, 5) identify the ideal configuration of sensors and algorithms to enhance safety, and 6) monitor the performance of perception and motion planning modules.

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“…The huge costs in health services, and the added burden on public finances due to road traffic injuries and fatalities, representing approximately 1–3% of GDP in most countries, have become increasingly socially unacceptable [ 2 ]. On the other hand, road crashes, previously regarded as random, unavoidable ‘accidents’, have been increasingly identified as a preventable public health problem due to the development of a better understanding of the nature of crashes over the past decades [ 3 , 4 , 5 , 6 ]. It is widely understood that drivers’ risky or abnormal behaviors are highly related to road crashes.…”

Section: Introductionmentioning

confidence: 99%

Driving Behavior Based Relative Risk Evaluation Using a Nonparametric Optimization Method

Bao

Tang

Shen

2021

IJERPH

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Evaluating risks when driving is a valuable method by which to make people better understand their driving behavior, and also provides the basis for improving driving performance. In many existing risk evaluation studies, however, most of the time only the occurrence frequency of risky driving events is considered in the time dimension and fixed weights allocation is adopted when constructing a risk evaluation model. In this study, we develop a driving behavior-based relative risk evaluation model using a nonparametric optimization method, in which both the frequency and the severity level of different risky driving behaviors are taken into account, and the concept of relative risk instead of absolute risk is proposed. In the case study, based on the data from a naturalistic driving experiment, various risky driving behaviors are identified, and the proposed model is applied to assess the overall risk related to the distance travelled by an individual driver during a specific driving segment, relative to other drivers on other segments, and it is further compared with an absolute risk evaluation. The results show that the proposed model is superior in avoiding the absolute risk quantification of all kinds of risky driving behaviors, and meanwhile, a prior knowledge on the contribution of different risky driving behaviors to the overall risk is not required. Such a model has a wide range of application scenarios, and is valuable for feedback research relating to safe driving, for a personalized insurance assessment based on drivers’ behavior, and for the safety evaluation of professional drivers such as ride-hailing drivers.

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A review of surrogate safety measures and their applications in connected and automated vehicles safety modeling

Cited by 210 publications

References 76 publications

Connected Vehicles: Technology Review, State of the Art, Challenges and Opportunities

Connected Vehicles: Technology Review, State of the Art, Challenges and Opportunities

A Literature Review of Performance Metrics of Automated Driving Systems for On-Road Vehicles

Driving Behavior Based Relative Risk Evaluation Using a Nonparametric Optimization Method

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