Computational modeling of driver pre-crash brake response, with and without off-road glances: Parameterization using real-world crashes and near-crashes

Svärd, Malin; Markkula, Gustav; Bärgman, Jonas; Victor, Trent

doi:10.1016/j.aap.2021.106433

Cited by 17 publications

(2 citation statements)

References 49 publications

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“…Two framework assumptions (shown in gray in Fig. 2 ) are shared among all our tested model variants: First, based on theories of motor primitives ( 32 ) and intermittent sensorimotor control ( 33 ), which have been shown to explain driver behavior in both routine and near-crash situations ( 34 , 35 ), and based on observations and models of stepwise adjustments to pedestrian walking speed ( 2 , 36 , 37 , 38 ), we model the longitudinal locomotion of driver and pedestrian as constructed from intermittent adjustments to acceleration and speed, respectively. Second, aligning with a long modeling tradition in psychology and neuroscience ( 21 ), we assume that agents decide what motor primitives to apply by estimating the value (or utility, or predicted reward) from applying each alternative action.…”

Section: Introductionmentioning

confidence: 99%

Explaining human interactions on the road by large-scale integration of computational psychological theory

et al. 2023

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When humans share space in road traffic, as drivers or as vulnerable road users, they draw on their full range of communicative and interactive capabilities. Much remains unknown about these behaviors, but they need to be captured in models if automated vehicles are to coexist successfully with human road users. Empirical studies of human road user behavior implicate a large number of underlying cognitive mechanisms, which taken together are well beyond the scope of existing computational models. Here, we note that for all of these putative mechanisms, computational theories exist in different subdisciplines of psychology, for more constrained tasks. We demonstrate how these separate theories can be generalized from abstract laboratory paradigms and integrated into a computational framework for modeling human road user interaction, combining Bayesian perception, a theory of mind regarding others’ intentions, behavioral game theory, long-term valuation of action alternatives, and evidence accumulation decision-making. We show that a model with these assumptions—but not simpler versions of the same model—can account for a number of previously unexplained phenomena in naturalistic driver–pedestrian road-crossing interactions, and successfully predicts interaction outcomes in an unseen data set. Our modeling results contribute to demonstrating the real-world value of the theories from which we draw, and address calls in psychology for cumulative theory-building, presenting human road use as a suitable setting for work of this nature. Our findings also underscore the formidable complexity of human interaction in road traffic, with strong implications for the requirements to set on development and testing of vehicle automation.

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Section: Introductionmentioning

confidence: 99%

Explaining human interactions on the road by large-scale integration of computational psychological theory

et al. 2023

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“…The work presented in this paper addresses only the first step of this crash scenario generation approach. Many other studies have analyzed the following vehicle's behavior during rear-end emergencies (crashes and near-crashes) by means of a driver response model [18], [19], [20], [21], [22], [23], [24], [25]. For example, Markkula et al [23] used a piecewise linear model and used driver glance behaviors to model the following vehicles' speed profiles in naturalistic rear-end emergencies.…”

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confidence: 99%

Modeling Lead-Vehicle Kinematics for Rear-End Crash Scenario Generation

Wu,

Flannagan,

Sander

et al. 2024

IEEE Trans. Intell. Transport. Syst.

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The use of virtual safety assessment as the primary method for evaluating vehicle safety technologies has emphasized the importance of crash scenario generation. One of the most common crash types is the rear-end crash, which involves a lead vehicle and a following vehicle. Most studies have focused on the following vehicle, assuming that the lead vehicle maintains a constant acceleration/deceleration before the crash. However, there is no evidence for this premise in the literature. This study aims to address this knowledge gap by thoroughly analyzing and modeling the lead vehicle's behavior as a first step in generating rear-end crash scenarios. Accordingly, the study employed a piecewise linear model to parameterize the speed profiles of lead vehicles, utilizing two rear-end pre-crash/near-crash datasets. These datasets were merged and categorized into multiple subdatasets; for each one, a multivariate distribution was constructed to represent the corresponding parameters. Subsequently, a synthetic dataset was generated using these distribution models and validated by comparison with the original combined dataset. The results highlight diverse lead-vehicle speed patterns, indicating that a more accurate model, such as the proposed piecewise linear model, is required instead of the conventional constant acceleration/deceleration model. Crashes generated with the proposed models accurately match crash data across the full severity range, surpassing existing lead-vehicle kinematics models in both severity range and accuracy. By providing more realistic speed profiles for the lead vehicle, the model developed in the study contributes to creating realistic rear-end crash scenarios and reconstructing real-life crashes.

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Rear-End Collision Risk Analysis for Autonomous Driving

Liang,

Ghazel,

et al. 2023

Computer Safety, Reliability, and Security. SAFECOMP 2023 Workshops

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Computational modeling of driver pre-crash brake response, with and without off-road glances: Parameterization using real-world crashes and near-crashes

Cited by 17 publications

References 49 publications

Explaining human interactions on the road by large-scale integration of computational psychological theory

Explaining human interactions on the road by large-scale integration of computational psychological theory

Modeling Lead-Vehicle Kinematics for Rear-End Crash Scenario Generation

Rear-End Collision Risk Analysis for Autonomous Driving

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