Situational influencing factors on understanding cooperative actions in automated driving

Stoll, Tanja; Lanzer, Mirjam; Baumann, Martin

doi:10.1016/j.trf.2020.03.006

Cited by 28 publications

(11 citation statements)

References 21 publications

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“…Driving behavior. Predicting and planning interactive behaviors in complex traffic situations presents a challenging task 116 . It is difficult to predict and arrange interactive behaviors in complex traffic scenarios.…”

Section: Intra-vehicle Networking (Ivn)mentioning

confidence: 99%

“…A consequence of the inherent constraints of these sensors is that AVs are more likely to make wrong decisions, which can result in fatal outcomes. At this stage, IVN technologies can compensate for sensor shortcomings and are more dependable, practicable, and efficient in boosting information interaction, resulting in improved AVs perception and planning skills and enhanced vehicle control116 . Inter-vehicle communication is only possible if significant messages that increase safety can be exchanged quickly and efficiently.…”

mentioning

confidence: 99%

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Multiple vehicle cooperation and collision avoidance in automated vehicles: survey and an AI-enabled conceptual framework

Muzahid

Kamarulzaman

Rahman

et al. 2023

Sci Rep

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Prospective customers are becoming more concerned about safety and comfort as the automobile industry swings toward automated vehicles (AVs). A comprehensive evaluation of recent AVs collision data indicates that modern automated driving systems are prone to rear-end collisions, usually leading to multiple-vehicle collisions. Moreover, most investigations into severe traffic conditions are confined to single-vehicle collisions. This work reviewed diverse techniques of existing literature to provide planning procedures for multiple vehicle cooperation and collision avoidance (MVCCA) strategies in AVs while also considering their performance and social impact viewpoints. Firstly, we investigate and tabulate the existing MVCCA techniques associated with single-vehicle collision avoidance perspectives. Then, current achievements are extensively evaluated, challenges and flows are identified, and remedies are intelligently formed to exploit a taxonomy. This paper also aims to give readers an AI-enabled conceptual framework and a decision-making model with a concrete structure of the training network settings to bridge the gaps between current investigations. These findings are intended to shed insight into the benefits of the greater efficiency of AVs set-up for academics and policymakers. Lastly, the open research issues discussed in this survey will pave the way for the actual implementation of driverless automated traffic systems.

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Section: Intra-vehicle Networking (Ivn)mentioning

confidence: 99%

mentioning

confidence: 99%

Multiple vehicle cooperation and collision avoidance in automated vehicles: survey and an AI-enabled conceptual framework

Muzahid

Kamarulzaman

Rahman

et al. 2023

Sci Rep

View full text Add to dashboard Cite

show abstract

“…However, some states in the United States, such as California, have more specific regulations in road traffic law, requiring drivers to use a turn signal at least 5 s before a lane change [4]. Proper turn signal usage is essential, as it could avoid many potential crashes [2] and also increase cooperative behaviour (such as opening up gaps for lane changing vehicles) by drivers in the target lane [1,5]. The use of proper turn signals is also imperative in the context of future mixed traffic scenarios, where AVs will coexist with conventional human-driven vehicles on shared roadways.…”

Section: Introductionmentioning

confidence: 99%

Exploring turn signal usage patterns in lane changes: A Bayesian hierarchical modelling analysis of realistic driving data

Jokhio,

Olleja,

Bärgman

et al. 2023

IET Intelligent Trans Sys

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Using turn signals to convey a driver's intention to change lanes provides a direct and unambiguous way of communicating with nearby drivers. Nonetheless, past research has indicated that drivers may not always use their turn signals before starting a lane change. In this study, realistic driving data are analyzed to investigate turn signal usage during lane changes on highways in and around Gothenburg, Sweden. Turn signal usage is examined and factors that influence it are identified by employing Bayesian hierarchical modelling. The study found that drivers used their turn signal before changing lanes in 60% of cases, after starting the lane change in 33% of cases, and did not use it at all in 7% of cases. The Bayesian hierarchical modelling results indicate that various factors, such as the speed and direction of lane changes and the presence of surrounding vehicles, influence the usage of turn signals. The study concludes that understanding the factors affecting turn signal usage is crucial for improving traffic safety in current and future mixed traffic with autonomous vehicles. The study discusses the implications of findings concerning increasing turn signal compliance through general policy‐making, improving existing in‐vehicle technologies and including turn signal usage in Pay‐As‐You‐Drive insurances.

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“…However, there is much less research on these contextual factors. The criticality of the situation and the scope of action, manipulated by the number of lanes or the available time for a driver to act, are context factors that influence driver's behavior in manual driving (Heesen et al, 2012;Stoll et al, 2020a;Stoll et al, 2020b) and takeover scenarios (Wu et al, 2019) as well as the acceptance of behavior choices of an automated vehicle (Stoll et al, 2021). With reducing the scope of action, the situation was perceived as more critical (Stoll et al, 2020b) and the acceptance of automated behavior increased (Stoll et al, 2021).…”

Section: Introductionmentioning

confidence: 99%

Intelligent Mobility in the City: The Influence of System and Context Factors on Drivers’ Takeover Willingness and Trust in Automated Vehicles

Lanzer¹,

Stoll²,

Colley

et al. 2021

Front. Hum. Dyn.

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Automated driving in urban environments not only has the potential to improve traffic flow and heighten driver comfort but also to increase traffic safety, particularly for vulnerable road users such as pedestrians. For these benefits to take effect, drivers need to trust and use automated vehicles. This decision is influenced by both system and context factors. However, it is not yet clear how these factors interact with each other, especially for automated driving in city scenarios with crossing pedestrians. Therefore, we conducted an online experiment in which participants (N = 68) experienced short automated rides from the driver’s perspective through an urban environment. In each of the presented videos, a pedestrian crossed the street in front of the automated vehicle while system and context factors were varied: 1) the crossing pedestrian’s intention was either visualized correctly (as crossing) or incorrectly (visualization missing) by the automated vehicle (system factor), 2) the pedestrian was either distracted by using a smartphone while crossing or not (context factor), and 3) the scenario was either more or less complex depending on the number of other vehicles and pedestrians being present (context factor). In situations with a system malfunction where the crossing pedestrian’s intention was not visualized, participants perceived the situation as more critical, had less trust in the automated system, and a higher willingness to take over control regardless of any context factors. However, when the system worked correctly, the crossing pedestrian’s smartphone usage came into play, especially in the less complex scenario. Participants perceived situations with a distracted pedestrian as more critical, trusted the system less, indicated a higher willingness to take over control, and were more uncertain about their decision. As this study demonstrates the influence of distracted pedestrians, more research is needed on context factors and their inclusion in the design of interfaces to keep drivers informed during automated driving in urban environments.

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Situational influencing factors on understanding cooperative actions in automated driving

Cited by 28 publications

References 21 publications

Multiple vehicle cooperation and collision avoidance in automated vehicles: survey and an AI-enabled conceptual framework

Multiple vehicle cooperation and collision avoidance in automated vehicles: survey and an AI-enabled conceptual framework

Exploring turn signal usage patterns in lane changes: A Bayesian hierarchical modelling analysis of realistic driving data

Intelligent Mobility in the City: The Influence of System and Context Factors on Drivers’ Takeover Willingness and Trust in Automated Vehicles

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