Should I Stay or Should I Go? Cognitive Modeling of Left-Turn Gap Acceptance Decisions in Human Drivers

Zgonnikov, Arkady; Abbink, David A.; Markkula, Gustav

doi:10.1177/00187208221144561

Cited by 23 publications

(14 citation statements)

References 102 publications

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“…We see multiple possible ways of introducing stochasticity in the framework to account for this. To name two: adding stochasticity could be done in the receiving of communication (translating perceptual information to an updated belief) by using evidence accumulation mechanisms [ 10 ] or additive noise, or by including noise directly in the risk perception. However, more work is needed to determine the best approach.…”

Section: Discussionmentioning

confidence: 99%

“…But it has so far mostly been done with a focus on single-driver behaviour, either in single-vehicle (e.g. [ 4 , 5 ]) or multi-vehicle scenarios such as car following [ 6 , 7 ], lane changing [ 8 , 9 ], and gap acceptance [ 10 , 11 ]. Most multi-vehicle approaches assume that the modelled driver responds to other traffic participants, but that they don’t respond in turn.…”

Section: Introductionmentioning

confidence: 99%

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Modelling communication-enabled traffic interactions

2023

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A major challenge for autonomous vehicles is handling interactions with human-driven vehicles—for example, in highway merging. A better understanding and computational modelling of human interactive behaviour could help address this challenge. However, existing modelling approaches predominantly neglect communication between drivers and assume that one modelled driver in the interaction responds to the other, but does not actively influence their behaviour. Here, we argue that addressing these two limitations is crucial for the accurate modelling of interactions. We propose a new computational framework addressing these limitations. Similar to game-theoretic approaches, we model a joint interactive system rather than an isolated driver who only responds to their environment. Contrary to game theory, our framework explicitly incorporates communication between two drivers and bounded rationality in each driver’s behaviours. We demonstrate our model’s potential in a simplified merging scenario of two vehicles, illustrating that it generates plausible interactive behaviour (e.g. aggressive and conservative merging). Furthermore, human-like gap-keeping behaviour emerged in a car-following scenario directly from risk perception without the explicit implementation of time or distance gaps in the model’s decision-making. These results suggest that our framework is a promising approach to interaction modelling that can support the development of interaction-aware autonomous vehicles.

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Section: Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Modelling communication-enabled traffic interactions

2023

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“…Evidence accumulation offers a well-established depiction of human behavior for some specific decisions [36], [37] and suggests that evidence for a particular response is integrated by single or multiple accumulators over time and by a rate known as drift rate which is the rate at which sensory information reaches a bound (a decision boundary) [23]. This model has been used for simulating and predicting driver gap acceptance in left-turns [38], pedestrian crossing decisions [22], [39], and AV-human interactions in take-over and crossing scenarios [40]. That said, while evidence accumulation models provide ample detail about the decisionmaking process, they do so for a very constrained set of tasks and are typically considered single-decision models suggesting they may not be able to account for all types of interaction scenarios.…”

Section: A Related Workmentioning

confidence: 99%

Driver-Pedestrian Interactions at Unsignalized Crossings Are Not in Line With the Nash Equilibrium

Kalantari,

Yang,

Lee

et al. 2023

IEEE Access

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Recent developments in vehicle automation require simulations of human-robot interactions in the road traffic context, which can be achieved by computational models of human behavior such as game theory. Game theory provides a good insight into road user behavior by considering agents' interdependencies. However, it is still unclear whether conventional game theory is suitable for modeling vehicle-pedestrian interactions at unsignalized locations or if more complex models like behavioral game theory are needed. Hence, we compared four game-theoretic models based on two different payoff formulations and two solving algorithms, to answer this question. Unlike the most previous studies that employed naturalistic datasets to test and validate such models, this study utilized a distributed simulation dataset to test and compare the models. The study was conducted by connecting a CAVE-based pedestrian simulator to a motion-based driving simulator to replicate the traffic scenarios for 32 pedestrian-driver pairs. The findings demonstrated that there is a high variability between participant pairs' behaviors. Our proposed behavioral game-theoretic model outperformed other models in predicting the interaction outcome. This translates to a decrease by 70% and 67% in the root mean squared error (RMSE) when compared to the baseline model, for marked and unmarked crossings, respectively. The model can also predict which interaction will take the longest time to resolve. According to our results, road users cannot be expected to behave in line with the Nash equilibrium of conventional game theory that underscores the complexity of human behavior with implications for the testing and development of automated vehicles.

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“…The noise in the velocity perception is inspired by evidence accumulation, a concept used in driver decisionmaking studies before [41,57]. Specifically, we assume that drivers update their perceived velocity of the other vehicle v p at every time step with an observation affected by noise…”

Section: Modelmentioning

confidence: 99%

Interactive merging behavior in a coupled driving simulator: Experimental framework and case study

Siebinga¹,

Zgonnikov²,

Abbink³

2022

Human Factors in Transportation

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Human highway-merging behavior is an important aspect when developing autonomous vehicles (AVs) that can safely and successfully interact with other road users. To design safe and acceptable human-AV interactions, the underlying mechanisms in human-human interactive behavior need to be understood. Exposing and understanding these mechanisms can be done using controlled driving simulator experiments. However, until now, such human-factors merging experiments have focused on aspects of the behavior of a single driver (e.g., gap acceptance) instead of on the dynamics of the interaction. Furthermore, existing experimental scenarios and data analysis tools (i.e., concepts like time-to-collision) are insufficient to analyze human-human interactive merging behavior. To help facilitate human-factors research on merging interactions, we propose an experimental framework consisting of a general simplified merging scenario and a set of three analysis tools: (1) a visual representation that captures the combined behavior of two participants and the safety margins they maintain in a single plot; (2) a signal (over time) that describes the level of conflict; and (3) a metric that describes the amount of time that was required to solve the merging conflict, called the conflict resolution time. In a case study with 18 participants, we used the proposed framework and analysis tools in a top-down view driving simulator where two human participants can interact. The results show that the proposed scenario can expose diverse behaviors for different conditions. We demonstrate that our novel visual representation, conflict resolution time, and conflict signal are valuable tools when comparing human behavior between conditions. Therefore, with its simplified merging scenario and analysis tools, the proposed experimental framework can be a valuable asset when developing driver models that describe interactive merging behavior and when designing AVs that interact with humans.

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Should I Stay or Should I Go? Cognitive Modeling of Left-Turn Gap Acceptance Decisions in Human Drivers

Cited by 23 publications

References 102 publications

Modelling communication-enabled traffic interactions

Modelling communication-enabled traffic interactions

Driver-Pedestrian Interactions at Unsignalized Crossings Are Not in Line With the Nash Equilibrium

Interactive merging behavior in a coupled driving simulator: Experimental framework and case study

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