A generic multi-scale framework for microscopic traffic simulation part II – Anticipation Reliance as compensation mechanism for potential task overload

J.W.C Van Lint,Simeon C Calvert,Wouter J Schakel

doi:10.1016/j.trb.2020.07.011

J.W.C Van Lint, Simeon C Calvert + Show 1 more

Open Access

https://doi.org/10.1016/j.trb.2020.07.011

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Abstract

The inclusion of human factors (HF) in mathematical models is proving crucial to allow complex driving behaviour and interactions to be explicitly considered to capture driving phenomena. An important area where such integration is required is for the role of anticipation by drivers to compensate for critical traffic situations. In this paper, we introduce the concept of Anticipation Reliance (AR), which acts as a demand lowering compensative effect for the driving task by relying more on anticipation. We implement AR in a generic multi-scale microscopic traffic modelling and simulation framework to explore and explain the effects of HF on traffic operations and safety in critical traffic situations. This concept addresses a disparity in the description of driver workload in relation to the execution of driving tasks in regard to the confidence that drivers place on tasks that are sub-consciously catered for. The crossover from HF to a mathematical description of this role of AR introduces a ground-breaking concept that explains and models the mechanisms that allow drivers to compensate and avoid accidents in many circumstances, even when driving errors or sub-optimal driving performance occurs. By and large, the HF effects can be subdivided in effects on perception and anticipation; effects on sensitivity and response to stimuli; and effects on personal attributes and characteristics. A key aspect of the framework are two intertwined driver-specific mental state variables—total workload and awareness—that bridge between classic collision-free idealized models for lane changing and car following, and HF models that explain under which conditions the performance of drivers deteriorates in terms of reaction time, sensitivity to stimuli or other parameters. In this paper, we focus on the awareness construct, as described by AR, and explore the effects. We prove the effectiveness of the approach with a case example that demonstrates the ability of the model to dissect a complex traffic situation with both longitudinal and lateral driving tasks, while endogenously considering human factors and that produces accident avoidance and occurrence within the same order of magnitude compared to real traffic accident statistics.

Highlights

While millions of road accidents occur each year, this number could well be significantly higher if drivers generally were not as good as they are at compensating for errors and unexpected changes in their driving environments (Strayer and Drew, 2004; Wickens et al, 2015; De Raedt and Ponjaert-Kristoffersen, 2000; Schömig, Metz, and Krüger, 2011)
The new idea we explore in this paper is that whereas task saturation may affect awareness, the inverse may be true (Fig. 4c), that is, drivers may rely on their anticipation capabilities for one task, while executing other tasks
Perception is a key aspect of the quality, i.e. how well can a driver perceive the environment relating to a certain task, and, what is for example the scanning frequency and glance time in regard to a task? (Salvucci and Macuga, 2002) Within this framework, we limit ourselves to stating that perception quality and anticipation confidence is relevant, without further explicitly stating how these are determined

Summary

Introduction

While millions of road accidents occur each year, this number could well be significantly higher if drivers generally were not as good as they are at compensating for errors and unexpected changes in their driving environments (Strayer and Drew, 2004; Wickens et al, 2015; De Raedt and Ponjaert-Kristoffersen, 2000; Schömig, Metz, and Krüger, 2011). Many accidents are prevented and can be categorized as near-misses (McKenna, Horswill, and Alexander, 2006) These mechanisms are all part of a human’s cognitive information processing, which for traffic is often described within the domain of human factors, and which is crucial for understanding driving mechanism for (near)-accidents (Wong and Huang, 2009). A logical and important expansion of this framework is the inclusion of the role of anticipation to allow a driver to take on more complex task combinations and to compensate for cognitive overloading This is the main contribution of this paper, to introduce Anticipation Reliance as a concept that allows this and that can be applied in human factor based microsimulation to achieve driver compensatory behaviour and to explain how drivers can deal with multiple tasks without always becoming cognitively oversaturated.

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