Toward Computational Simulations of Behavior During Automated Driving Takeovers: A Review of the Empirical and Modeling Literatures

Objective: This article provides a review of empirical studies of automated vehicle takeovers and driver modeling to identify influential factors and their impacts on takeover performance and suggest driver models that can capture them. Background: Significant safety issues remain in automated-to-manual transitions of vehicle control. Developing models and computer simulations of automated vehicle control transitions may help designers mitigate these issues, but only if accurate models are used. Selecting accurate models requires estimating the impact of factors that influence takeovers. Method: Articles describing automated vehicle takeovers or driver modeling research were identified through a systematic approach. Inclusion criteria were used to identify relevant studies and models of braking, steering, and the complete takeover process for further review. Results: The reviewed studies on automated vehicle takeovers identified several factors that significantly influence takeover time and post-takeover control. Drivers were found to respond similarly between manual emergencies and automated takeovers, albeit with a delay. The findings suggest that existing braking and steering models for manual driving may be applicable to modeling automated vehicle takeovers. Conclusion: Time budget, repeated exposure to takeovers, silent failures, and handheld secondary tasks significantly influence takeover time. These factors in addition to takeover request modality, driving environment, non-handheld secondary tasks, level of automation, trust, fatigue, and alcohol significantly impact post-takeover control. Models that capture these effects through evidence accumulation were identified as promising directions for future work. Application: Stakeholders interested in driver behavior during automated vehicle takeovers may use this article to identify starting points for their work

Disenclosed time regimes and spatial concentrations of economics. The creative sector in Hamburg's Schanzenviertel from a time-geographical perspective

Disenclosed time regimes and spatial concentrations of economics. The creative sector in Hamburg's Schanzenviertel from a time-geographical perspective. In recent years the spatial and temporal organization of everyday life has been transformed by the flexibilisation of economic relations and a growing variety of the ways how individuals coordinate their professional and private spheres and activities. Central elements of this transformation are (1) disenclosures of fixed socio-structural, temporal and spatial boundaries, (2) technical, social and cultural accelerations and (3) a growing subject-orientation within work and life conditions. Cities offer places where these transformations are well observable. These places provide both diverse infrastructures and images as material and symbolic frames for local socioeconomic processes: multiple encounters, mutual learning processes and work-life-combinations. The analysis of Hamburg's Schanzenviertel shows the explanatory power of a broadened time geographical concept in elucidating those new socioeconomic spaces. We add the concept of coupling opportunities to the traditional constraints-approach in order to show why "the urban" under the label of the creative city Should be understood as a specific form of organizing the everyday as well as the work life. Interrelations of subjective and structural, conditions clarify the subject-orientation within spatial economics as both system-related constraints and chances for individual self-determination. This analysis opens up a critical perspective on the selectivity and spatial inequalities within the creative city debate

Steering or braking avoidance response in SHRP2 rear-end crashes and near-crashes: A decision tree approach

Objective The paper presents a systematic analysis of drivers’ crash avoidance response during crashes and near-crashes and developed a machine learning-based predictive model that can determine driver maneuver using pre-incident driver behavior and driving context. Methods We analyzed 286 naturalistic rear-end crashes and near-crashes from the SHRP2 naturalistic driving study. All the events were manually reduced using face video (face and forward) and kinematic responses. In this paper, we developed new reduction variables that enhanced the understanding of drivers’ gaze behavior and roadway attention behavior during these events. These features reflected how the event criticality, measured using time to collision, related to drivers’ pre-incident behavior (secondary behavior, gaze behavior), and drivers’ perception of the event (physical reaction and maneuver). The imperative understanding of such relations was validated using a random forest- (RF) based classifier, which efficiently predicted if a driver was going to brake or change the lane as an avoidance maneuver. Results The RF presented in this paper effectively explored the nonlinear patterns in the data and was highly accurate (∼96 %) in its prediction. A further analysis of the RF model showed that six features played a pivotal role in the decision logic. These included the drivers’ last glance duration before the event, last glance eccentricity, duration of ‘eyes on road’ immediately before the event, the time instance and criticality when the driver perceives the threat as well as acknowledge the threat, and possibility of an escape path in the adjacent lane. Using partial dependency plots, we also showed how different thresholds of these feature variables determined the drivers’ maneuver intention. Conclusions In this paper we analyzed driving context, drivers’ behavior, event criticality, and drivers’ response in a unified structure to predict their avoidance response. To the best of our knowledge, this is the first such effort where large-scale naturalistic data (crashes and near crashes) was analyzed for prediction of drivers’ maneuver and determined key behavioral and contextual factors that contribute to this avoidance maneuver