This paper describes the architecture and implementation of an autonomous passenger vehicle designed to navigate using locally perceived information in preference to potentially inaccurate or incomplete map data. The vehicle architecture was designed to handle the original DARPA Urban Challenge requirements of perceiving and navigating a road network with segments defined by sparse waypoints. The vehicle implementation includes many heterogeneous sensors with significant communications and computation bandwidth to capture and process high-resolution, high-rate sensor data. The output of the comprehensive environmental sensing subsystem is fed into a kinodynamic motion planning algorithm to generate all vehicle motion. The requirements of driving in lanes, three-point turns, parking, and maneuvering through obstacle fields are all generated with a unified planner. A key aspect of the planner is its use of closed-loop simulation in a rapidly exploring randomized trees algorithm, which can randomly explore the space while efficiently generating smooth trajectories in a dynamic and uncertain environment. The overall system was realized through the creation of a powerful new suite of software tools for message passing, logging, and visualization. These innovations provide a strong platform for future research in autonomous driving in global positioning system-denied and highly dynamic environments with poor a priori information. C 2008 Wiley Periodicals, Inc.

This paper describes the architecture and implementation of an autonomous passenger vehicle designed to navigate using locally perceived information in preference to potentially inaccurate or incomplete map data. The vehicle architecture was designed to handle the original DARPA Urban Challenge requirements of perceiving and navigating a road network with segments defined by sparse waypoints. The vehicle implementation includes many heterogeneous sensors with significant communications and computation bandwidth to capture and process high-resolution, high-rate sensor data. The output of the comprehensive environmental sensing subsystem is fed into a kino-dynamic motion planning algorithm to generate all vehicle motion. The requirements of driving in lanes, three-point turns, parking, and maneuvering through obstacle fields are all generated with a unified planner. A key aspect of the planner is its use of closed-loop simulation in a Rapidly-exploring Randomized Trees (RRT) algorithm, which can randomly explore the space while efficiently generating smooth trajectories in a dynamic and uncertain environment. The overall system was realized through the creation of a powerful new suite of software tools for message-passing, logging, and visualization. These innovations provide a strong platform for future research in autonomous driving in GPS-denied and highly dynamic environments with poor a priori information.

This project was funded by the National Institute for Health Research (NIHR) Health Technology Assessment programme and will be published in full in ; Vol. 22, No. 33. See the NIHR Journals Library website for further project information.

More TBI-VR participants returned to work than UC. People with moderate/severe TBI benefitted most. This positive trend was achieved without greatly increased health costs, suggesting cost-effectiveness. This study justifies the need for and can inform a definitive Randomized Controlled Trial (RCT).

The objectives of this study were to examine the agreement between 5 ergonomic risk assessment methods calculated on the basis of quantitative exposure measures and to examine the ability of the methods to correctly classify 4 at risk jobs. Surface electromyography and electrogoniometry were used to record the physical exposures of 87 sawmill workers performing 4 repetitive jobs. Five ergonomic risk assessment tools (rapid upper limb assessment [RULA], rapid entire body assessment [REBA], American conference of governmental industrial hygienist's threshold limit value for mono-task hand work [ACGIH TLV], strain index [SI], and concise exposure index [OCRA]) were calculated. Dichotomization of risk to no risk and at risk resulted in high agreement between methods. Percentage of perfect agreement between methods when 3 levels of risk were considered was moderate and varied by job. Of the methods examined, the RULA and SI were best (correct classification rates of 99 and 97% respectively). The quantitative ACGIH-TLV for mono-task hand work and Borg scale were worst (misclassification rates of 86 and 28% respectively).