Manual drivers’ experience and driving behavior in repeated interactions with automated Level 3 vehicles in mixed traffic on the highway

Stange, Vanessa; Kühn, Matthias; Vollrath, Mark

doi:10.1016/j.trf.2022.04.019

Cited by 15 publications

(4 citation statements)

References 19 publications

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“…However, the implementation of CAVs in real traffic where they have to interact with nearby human-driven vehicles is still in development and far from the full experimental validation [51]. While many studies have shown a myriad of prominent applications on vehicle design requirements, V2V or V2I communications, jam dynamics, CAV platooning, and reduced infrastructure footprint on highways (e.g., [7,9,[52][53][54]), only a few studies have been undertaken to determine how CAVs can negotiate intersections and roundabouts, a fact which also clearly influences land-use and urban planning (e.g., [30,55,56]).…”

Section: Discussionmentioning

confidence: 99%

A Methodological Framework to Assess Road Infrastructure Safety and Performance Efficiency in the Transition toward Cooperative Driving

et al. 2023

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There is increasing interest in connected and automated vehicles (CAVs), since their implementation will transform the nature of transportation and promote social and economic change. Transition toward cooperative driving still requires the understanding of some key questions to assess the performances of CAVs and human-driven vehicles on roundabouts and to properly balance road safety and traffic efficiency requirements. In this view, this paper proposes a simulation-based methodological framework aiming to assess the presence of increasing proportions of CAVs on roundabouts operating at a high-capacity utilization level. A roundabout was identified in Palermo City, Italy, and built in Aimsun (version 20) to describe the stepwise methodology. The CAV-based curves of capacity by entry mechanism were developed and then used as target capacities. To calibrate the model parameters, the capacity curves were compared with the capacity data simulated by Aimsun. The impact on the safety and performance efficiency of a lane dedicated to CAVs was also examined using surrogate measures of safety. The paper ends with highlighting a general improvement with CAVs on roundabouts, and with providing some insights to assess the advantages of the automated and connected driving technologies in transitioning to smarter mobility.

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Section: Discussionmentioning

confidence: 99%

A Methodological Framework to Assess Road Infrastructure Safety and Performance Efficiency in the Transition toward Cooperative Driving

et al. 2023

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“…In imminent mixed traffic, this behavioral mismatch between AVs and human drivers may pose challenges for human drivers and require them to change their own driving behavior. Indeed, current empirical evidence demonstrates that joining mixed traffic poses driving challenges for human drivers (Chityala et al, 2020;Lee et al, 2018), prompts them to engage in more aggressive maneuvers (Lee et al, 2018) and riskier behaviors such as maintaining shorter following distances (Stange et al, 2022), and makes them feel less comfortable (Stange et al, 2022). Behavioral incompatibility not only poses challenges to traffic flow but also jeopardizes traffic safety.…”

Section: Behavioral Incompatibilitymentioning

confidence: 99%

Machines meet humans on the social road: Risk implications

Liu

2023

Risk Analysis

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Human drivers and machine drivers (i.e., automated vehicles or AVs) will share roads and interact with each other, creating mixed traffic. In this perspective, we develop two mental models about them and their social interactions, aiming to understand the risk implications of AVs and mixed traffic. Based on Mental Model I (i.e., machine drivers are superior drivers without human weaknesses), many simulation‐based safety assessments, which often overlook or oversimplify human‐AV social interactions, have predicted significant safety benefits when machine drivers interact with or replace human drivers. In contrast, Mental Model II considers human and machine drivers as heterogeneous and incompatible, suggesting that their interactions may lead to unexpected and occasionally negative outcomes, particularly in imminent mixed traffic. This perspective gains support from recent comparative empirical studies that employ various methods such as survey experiments, driving simulators, test‐tracks, on‐road observations, and AV accident analysis. These studies provide initial evidence of emerging traffic risks arising from human‐AV social interactions, including human drivers’ aggression and road rage toward AVs, human drivers exploiting AVs, AVs exerting negative peer influences on human drivers, and their incompatibility increasing human drivers’ challenges in joining mixed traffic and thus risky behaviors. We propose specific suggestions to mitigate problematic human‐AV social interactions and the associated emerging risks.

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“…The social aspects are crucial for wide acceptance of self-driving cars and are strictly related to the technical level reached by autonomous vehicles (AVs). In a recent statistical study on a simulator, manual drivers in scenarios with autonomous vehicles have experienced safety concerns, average speed reduction, and more safety issues during specific manoeuvres [10]. Most AV designs include vehicle-to-vehicle (V2V), vehicle-to-infrastructure (V2I), or in general, vehicle-to-everything (V2x) communication systems.…”

Section: Introductionmentioning

confidence: 99%

Architecture and Potential of Connected and Autonomous Vehicles

Pipicelli,

Gimelli,

Sessa

et al. 2024

Vehicles

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The transport sector is under an intensive renovation process. Innovative concepts such as shared and intermodal mobility, mobility as a service, and connected and autonomous vehicles (CAVs) will contribute to the transition toward carbon neutrality and are foreseen as crucial parts of future mobility systems, as demonstrated by worldwide efforts in research and industry communities. The main driver of CAVs development is road safety, but other benefits, such as comfort and energy saving, are not to be neglected. CAVs analysis and development usually focus on Information and Communication Technology (ICT) research themes and less on the entire vehicle system. Many studies on specific aspects of CAVs are available in the literature, including advanced powertrain control strategies and their effects on vehicle efficiency. However, most studies neglect the additional power consumption due to the autonomous driving system. This work aims to assess uncertain CAVs’ efficiency improvements and offers an overview of their architecture. In particular, a combination of the literature survey and proper statistical methods are proposed to provide a comprehensive overview of CAVs. The CAV layout, data processing, and management to be used in energy management strategies are discussed. The data gathered are used to define statistical distribution relative to the efficiency improvement, number of sensors, computing units and their power requirements. Those distributions have been employed within a Monte Carlo method simulation to evaluate the effect on vehicle energy consumption and energy saving, using optimal driving behaviour, and considering the power consumption from additional CAV hardware. The results show that the assumption that CAV technologies will reduce energy consumption compared to the reference vehicle, should not be taken for granted. In 75% of scenarios, simulated light-duty CAVs worsen energy efficiency, while the results are more promising for heavy-duty vehicles.

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Manual drivers’ experience and driving behavior in repeated interactions with automated Level 3 vehicles in mixed traffic on the highway

Cited by 15 publications

References 19 publications

A Methodological Framework to Assess Road Infrastructure Safety and Performance Efficiency in the Transition toward Cooperative Driving

A Methodological Framework to Assess Road Infrastructure Safety and Performance Efficiency in the Transition toward Cooperative Driving

Machines meet humans on the social road: Risk implications

Architecture and Potential of Connected and Autonomous Vehicles

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