Integration of automated vehicles in mixed traffic: Evaluating changes in performance of following human-driven vehicles.
TL;DR: In this article, the authors investigated the effects of AVs on the behavior of a following human-driver in mixed traffic streams and found that a driver that follows an AV exhibits lower driving volatility in terms of speed and acceleration, which represents more stable traffic flow behavior and lower crash risk.
About: This article is published in Accident Analysis & Prevention.The article was published on 2021-03-01. It has received 35 citations till now. The article focuses on the topics: Poison control & Traffic flow.
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TL;DR: Fatigue and deviation to the left were found as the most important contributing factors that lead to fatal crashes when the large truck-driver is at fault.
50 citations
TL;DR: Real-time risk assessment studies have investigated a limited length of corridors, however, the necessity of assessing the safety performance of Connected Vehicles (CVs) requires looking into an en...
Abstract: Real-time risk assessment studies have investigated a limited length of corridors. However, the necessity of assessing the safety performance of Connected Vehicles (CVs) requires looking into an en...
28 citations
TL;DR: In this article , a method for real-time evaluation of road safety, in which traffic states and conflicts are combined to explore the internal relationship based on high-resolution trajectory data, is proposed.
23 citations
TL;DR: In this article, the authors applied a new methodology to capture variation in crashes in both space and time by using Geographically and Temporally Weighted Regression (GTWR) models for the localization of SPFs.
18 citations
TL;DR: In this paper , the authors investigated the possibility of detecting crashes using Basic Safety Messages (BSMs) in controlled high-fidelity driving simulator experiments, and two driving simulator scenarios were designed to simulate Run-off-Road (ROR), and Rear-End (RE) crashes.
Abstract: Connected Vehicles (CVs) technology has provided large-scale driving database embedded in Basic Safety Messages (BSMs). This valuable data source can shed more light on tracking individual driving behaviors to detect crashes. This study delves into the possibility of detecting crashes using BSMs in controlled high-fidelity driving simulator experiments. To this end, two driving simulator scenarios were designed to simulate Run-off-Road (ROR), and Rear-End (RE) crashes. Twenty-four professional truck drivers were recruited to drive the scenarios. In each scenario, crash and non-crash cases were identified from vehicles’ trajectories, resulting in four study cases. Drivers’ behaviors were quantified by characterizing two Kinematic-based Surrogate Measures of Safety (K-SMoS), namely Absolute value of Derivative of Instantaneous Acceleration (ADInstAccel) and Absolute value of Derivative of Steering (ADSteering). Extreme defensive driving volatilities under crash and non-crash cases were modeled by extreme value analysis of K-SMoS and fitting their associated Generalized Extreme Value (GEV) distributions under Bayesian inference. Accordingly, for each K-SMoS, the crash detection was formulated as a binary classification between two K-SMoS GEV continuous distributions under crash and corresponding non-crash conditions. Qualitative uncertainty analysis of joint posterior density distributions of GEVs’ parameters revealed a higher uncertainty of extreme driving behaviors in crash conditions. Regardless, notable relative increases in the central tendency of extreme K-SMoS in crash compared to non-crash conditions were found, implying the possibility of crash detection by tracking extreme drivers’ behaviors using trajectory-level observations. This visual inference was affirmed by the result of binary classification of GEV distributions associated with K-SMoS. Depending on the crash type and K-SMoS, 71% to 81% accuracy in crash detection was obtained, where ADSteering outperformed ADInstAccel in terms of the discriminative ability. Besides, using sensitivity–specificity analysis, the optimal threshold of 1.24 (rad/s) and 1.31 (m/s3), respectively, for ADSteering and ADInstAccel, were identified to detect crashes. These findings can potentially enhance CVs' automation level in spatiotemporally identifying crash-prone conditions to disseminate distress notifications. Furthermore, the introduced methodology can be a complementary one to what has been followed in the crash detection domain.
15 citations
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TL;DR: In this article, the authors proposed a nationally recognized licensing framework for AVs, determining appropriate standards for liability, security, and data privacy, which can be used to improve vehicle safety, congestion, and travel behavior.
Abstract: Autonomous vehicles (AVs) represent a potentially disruptive yet beneficial change to our transportation system. This new technology has the potential to impact vehicle safety, congestion, and travel behavior. All told, major social AV impacts in the form of crash savings, travel time reduction, fuel efficiency and parking benefits are estimated to approach $2000 to per year per AV, and may eventually approach nearly $4000 when comprehensive crash costs are accounted for. Yet barriers to implementation and mass-market penetration remain. Initial costs will likely be unaffordable. Licensing and testing standards in the U.S. are being developed at the state level, rather than nationally, which may lead to inconsistencies across states. Liability details remain undefined, security concerns linger, and without new privacy standards, a default lack of privacy for personal travel may become the norm. The impacts and interactions with other components of the transportation system, as well as implementation details, remain uncertain. To address these concerns, the federal government should expand research in these areas and create a nationally recognized licensing framework for AVs, determining appropriate standards for liability, security, and data privacy.
2,053 citations
TL;DR: The authors study the impacts of CACC for a highway-merging scenario from four to three lanes and show an improvement of traffic-flow stability and a slight increase in Trafficflow efficiency compared with the merging scenario without equipped vehicles.
Abstract: Cooperative adaptive cruise control (CACC) is an extension of ACC. In addition to measuring the distance to a predecessor, a vehicle can also exchange information with a predecessor by wireless communication. This enables a vehicle to follow its predecessor at a closer distance under tighter control. This paper focuses on the impact of CACC on traffic-flow characteristics. It uses the traffic-flow simulation model MIXIC that was specially designed to study the impact of intelligent vehicles on traffic flow. The authors study the impacts of CACC for a highway-merging scenario from four to three lanes. The results show an improvement of traffic-flow stability and a slight increase in traffic-flow efficiency compared with the merging scenario without equipped vehicles
1,347 citations
TL;DR: This paper explains the initiatives for automation in different levels of transportation system with a specific emphasis on the vehicle-level automation, and the impact of automation/warning systems on each of the above-mentioned factors.
Abstract: This paper looks into recent developments and research trends in collision avoidance/warning systems and automation of vehicle longitudinal/lateral control tasks. It is an attempt to provide a bigger picture of the very diverse, detailed and highly multidisciplinary research in this area. Based on diversely selected research, this paper explains the initiatives for automation in different levels of transportation system with a specific emphasis on the vehicle-level automation. Human factor studies and legal issues are analyzed as well as control algorithms. Drivers' comfort and well being, increased safety, and increased highway capacity are among the most important initiatives counted for automation. However, sometimes these are contradictory requirements. Relying on an analytical survey of the published research, we will try to provide a more clear understanding of the impact of automation/warning systems on each of the above-mentioned factors. The discussion of sensory issues requires a dedicated paper due to its broad range and is not addressed in this paper.
823 citations
TL;DR: In this article, the authors identify specific mechanisms through which automation may affect travel and energy demand and resulting GHG emissions and bring them together using a coherent energy decomposition framework, and explore the net effects of automation on emissions through several illustrative scenarios.
Abstract: Experts predict that new automobiles will be capable of driving themselves under limited conditions within 5–10 years, and under most conditions within 10–20 years. Automation may affect road vehicle energy consumption and greenhouse gas (GHG) emissions in a host of ways, positive and negative, by causing changes in travel demand, vehicle design, vehicle operating profiles, and choices of fuels. In this paper, we identify specific mechanisms through which automation may affect travel and energy demand and resulting GHG emissions and bring them together using a coherent energy decomposition framework. We review the literature for estimates of the energy impacts of each mechanism and, where the literature is lacking, develop our own estimates using engineering and economic analysis. We consider how widely applicable each mechanism is, and quantify the potential impact of each mechanism on a common basis: the percentage change it is expected to cause in total GHG emissions from light-duty or heavy-duty vehicles in the U.S. Our primary focus is travel related energy consumption and emissions, since potential lifecycle impacts are generally smaller in magnitude. We explore the net effects of automation on emissions through several illustrative scenarios, finding that automation might plausibly reduce road transport GHG emissions and energy use by nearly half – or nearly double them – depending on which effects come to dominate. We also find that many potential energy-reduction benefits may be realized through partial automation, while the major energy/emission downside risks appear more likely at full automation. We close by presenting some implications for policymakers and identifying priority areas for further research.
668 citations
Journal Article•
TL;DR: A scale of danger is proposed to be applied to a traffic event to FACILITATE OBJECTIVE MEASUREMENT and SUBSEQUENT DETECTION of NEAR-MISS SITUATIONS and may be used to STANDARDIZE HUMAN OBSERVER JUDGEMENT of DANGEROUS MANEUVERS and make near-mISS monitoring a viable alternative to traffic safety DETERMINation.
Abstract: NEAR-MISS TRAFFIC EVENTS HAVE BEEN CONSIDERED BUT NOT ADOPTED AS A TRAFFIC SAFETY TOOL BECAUSE OF THE HIGH DEGREE OF SUBJECTIVITY INVOLED WITH THEIR IDENTIFICATION. A SCALE OF DANGER MAY BE APPLIED TO A TRAFFIC EVENT TO FACILITATE OBJECTIVE MEASUREMENT AND SUBSEQUENT DETECTION OF NEAR-MISS SITUATIONS. THE UNIT PROPOSED HERE FOR THIS DANGER SCALE IS THE TIME MEASURED UNTIL COLLISION BETWEEN TWO VEHICLES INVOLVED IN THE UNSAFE EVENT. THIS MEASURE, COMPUTED FROM FILMS TAKEN WITH THE TRAFFIC SENSING AND SURVEILLANCE SYSTEM OF THE FEDERAL HIGHWAY ADMINISTRATION AT AN URBAN INTERSECTION, IS AN ADEQUATE UNIT TO RATE THE DANGER OF ALMOST ANY TRAFFIC EVENT. IT MAY BE USED TO STANDARDIZE HUMAN OBSERVER JUDGEMENT OF DANGEROUS MANEUVERS AND, THEREFORE, MAKE NEAR-MISS MONITORING A VIABLE ALTERNATIVE TO TRAFFIC SAFETY DETERMINATION. /AUTHOR/
627 citations