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

Abstract

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.

Social Interaction‐Aware Dynamical Models and Decision‐Making for Autonomous Vehicles

“…[ 10 ] One challenge is a lack of innovative theories about how HRUs interact. [ 11 ] This is a difficult task, as the theories to be developed are not limited to predicting and modeling HRUs’ behaviour but also exploring behaviour patterns and their underlying mechanisms. Integrating AVs into road traffic as seamlessly as humans would require more advanced behaviour theories and models.…”

“…[84,[88][89][90] Integrated largescale psychological theories to explain pedestrian crossing decisions in detail (Figure 6e). [11] Additionally, game theory has also been applied to model crossing decisions when pedestrians negotiate the right of way with vehicles. Conventional game theory, [91] Sequential Chicken (SC) game, [92] and Dual Accumulator (DA) game [93] were utilised to characterise the dynamic crossing decisions.…”

“…Cognitive models outline the internal state flow and motive that regulates the driver's behaviour as a psychological human being. [11,20]…”

“…[ 34,69 ] Recent research suggested that pedestrians use as a crucial visual cue to observe approaching vehicles. [ 11,34 ] However, while provides spatial information, it does not convey when the vehicle arrives at the pedestrian's position. [ 70 ] In crossing scenarios with yielding vehicles, pedestrians need temporal information to estimate whether the vehicle can stop in time.…”

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

“…One domain in which human decisions are inherently dynamic is traffic: human drivers and pedestrians make safety-critical decisions in complex dynamic scenes on a daily basis. Recent research has examined the cognitive mechanisms behind the decision-making processes underlying traffic decisions, revealing that evidence accumulation models can explain decisions and response times observed in a variety of tasks such as pedestrian crossing [20,21], left turn maneuvers [22,23], and overtaking [24]. In the context of lane-change decisions, driver uncertainty judgments (defined as "driver's difficulty to make appropriate decisions of either changing the lane or not in a given lane change situation") have been shown to depend on kinematics of vehicles in the traffic scene [25,26].…”