Effects of Performance Pressure on Response Inhibition Performance

Previous research suggests that psychological pressure tends to exert detrimental effects on action-oriented cognitive tasks. However, the effect of psychological pressure on inhibitory cognitive processes has been relatively overlooked. Consequently, the goal of this study was to examine the effect of psychological pressure on response inhibition performance. Participants (N = 125) were assigned to either a time pressure condition or control condition, and then completed the Stop Signal Task, which tests response inhibition. Outcome variables of interest were stop accuracy, stop signal reaction time, and post error slowing. The results from the study indicated that time pressure significantly impaired stop signal accuracy relative to the control condition. However, time pressure did not affect stop signal reaction time or post error slowing. This study conforms to the distraction theory of performance pressure. From this study, the observed effects detail what can be seen from this type of pressure. With this information, studies can be conducted on other types of performance pressure to expand the knowledge of those effects

Road departure crash warning system field operational test: methodology and results. Volume 1: technical report

This report summarizes results from the Intelligent Vehicle Initiative (IVI) Road Departure Crash Warning System Field Operational Test (RDCW FOT) project. This project was conducted under a cooperative agreement between the U.S. Dept. of Transportation and the University of Michigan Transportation Research Institute, along with its partners, Visteon Corporation and AssistWare Technologies. Road departure crashes account for 15,000 fatalities annually in the U.S. This project developed, validated, and field-tested a set of technologies intended to warn drivers in real time when the driver was drifting from their lane, and a curve-speed warning system designed to provide alerts to help driver slow down when approaching a curve too fast to safely negotiate the curve This report describes the field operational test of the system and subsequent analysis of the data to address the suitability of similar systems for widespread deployment within the U.S. passenger-vehicle fleet. Two areas were addressed: safety-related changes in driver performance including behavior that may be attributed to the system, and levels of driver acceptance in key areas. Testing used 11 passenger sedans equipped with RDCW and a data acquisition system that compiled a massive set of numerical, video, and audio data. Seventy-eight drivers each drove a test vehicle, unsupervised, for four weeks. The resulting data set captured 83,000 miles of driving, with over 400 signals captured at 10 Hz or faster. Analysis of the data shows that with the RDCW system active, relative to the baseline condition, drivers improved lanekeeping by remaining closer to the lane center and reducing the number of excursions near or beyond the lane edges. In addition, turn signal use increased dramatically. The data, however, were unable to confirm a change in driver’s curvetaking behaviors that could have been attributed to the curve speed warning system. Driver acceptance was generally positive in relation to the lateral drift component of the system, with reactions to the curve speed warning system being rather mixed. Many additional results and insights are documented in the report.National Highway Traffic Safety Administrationhttp://deepblue.lib.umich.edu/bitstream/2027.42/49242/1/99788.pd

The size that fits no-one. European monetarism reconsidered

The size that fits no-one. European monetarism reconsidere

Harnessing Big Data for Characterizing Driving Volatility in Instantaneous Driving Decisions – Implications for Intelligent Transportation Systems

This dissertation focuses on combining connected vehicles data, naturalistic driving sensor and telematics data, and traditional transportation data to prospect opportunities for engineering smart and proactive transportation systems.The key idea behind the dissertation is to understand (and where possible reduce) “driving volatility” in instantaneous driving decisions and increase driving and locational stability. As a new measure of micro driving behaviors, the concept of “driving volatility” captures the extent of variations in driving, especially hard accelerations/braking, jerky maneuvers, and frequent switching between different driving regimes. The key motivation behind analyzing driving volatility is to help predict what drivers will do in the short term. Consequently, this dissertation develops a “volatility matrix” which takes a systems approach to operationalizing driving volatility at different levels, trip-based volatility, location-based volatility, event-based volatility, and driver-based volatility. At the trip-level, the dynamics of driving regimes extracted from Basic Safety Messages transmitted between connected vehicles are analyzed at a microscopic level, and where the interactions between microscopic driving decisions and ecosystem of mapped local traffic states in close proximity surrounding the host vehicle are characterized. Another new idea relates to extending driving volatility to specific network locations, termed as “location-based volatility”. A new methodology is proposed for combining emerging connected vehicles data with traditional transportation data (crash, traffic, road geometrics data, etc.) to identify roadway locations where traffic crashes are waiting to happen. The idea of event-based and driver-based volatility introduces the notion that volatility in longitudinal and lateral directions prior to involvement in safety critical events (crashes/near-crashes) can be a leading indicator of proactive safety.Overall, by studying driving volatility from different lenses, the dissertation contributes to the scientific analysis of real-world connected vehicles data, and to generate actionable knowledge relevant to the design of smart and intelligent transportation systems. The concept of driving volatility matrix provides a systems framework for characterizing the health of three fundamental elements of a transportation system: health of driver, environment, and the vehicle. The implications of the findings and potential applications to proactive network level screening, customized driver assist and control systems, driving performance monitoring are discussed in detail