Safety

This paper presents the results of a review of commercial vehicle safety studies conducted to provide a better understanding of the current commercial vehicle safety problem. It outlines the approach used to conduct the review, summarizes the findings and provides suggestions for future studies based on the conclusions reached during the review. It is organized into the following topics:

• Introduction
• Approach
• Findings
• Conclusions
• Recommendations
• References

The findings from the review identified strengths and weaknesses in safety analysis in the areas of subject selection, data collection and completeness of study.

The study concluded that improvements can be made in each of these areas. The study provides recommendations on additional areas of study and changes in practice and introduces some new techniques for focusing safety management efforts and research.

The Johns Hopkins University

Presented at the 11th ITS Annual Conference and Exposition, June 4-7, 2001 Miami Beach, Florida

We describe experiments and results of four of the potentially nearest term means to realize a cooperative collision avoidance system, which we regard as either a supplement to or a simple replacement of present single vehicle-based systems. Experiments for each ranged from laboratory tests only (fluorescent paint), field tests only (roadside-mounted corner cubes), and a combination of laboratory and field tests (passive license plates, light emitting diode brake light messaging). These technologies all focus on improving the signal-to-noise ratio of a collision avoidance sensor. The LED brakelight messaging and passive license plates increase the signal, by making it easier to detect real vehicles on the roadway (and, in the case of LED brakelight messaging, to provide information on the trajectory of that vehicle). Corner Cubes serve to mark clutter, such as bridge abutments or overpasses, that cannot be moved. Fluorescent paint serves to improve the recognition of the lane markings, for localization. We believe that experimental results point toward further proof-of-concept refinements, but in general, that these systems potentially represent technologically sound cooperative vehicle-roadway components and that indeed, “sensor friendly” systems, when put to the test, can eventually translate into significant benefit in terms of lives saved.

California PATH, University of California at Berkeley

Robotics Institute, Carnegie Mellon University

Presented at the 11th ITS Annual Conference and Exposition, June 4-7, 2001 Miami Beach, Florida

The most significant federal investment in intelligent vehicle R&D in the U.S. is being conducted by the US Department of Defense (DOD) through the Demo III autonomous scout vehicle program. Demo III is an aggressive development effort in autonomous tactical ground vehicle technology R&D.

Robotics has been identified by numerous DOD studies as a key enabling technology for future military operational concepts. The Demo III program is a multiyear effort encompassing technology development and demonstration on testbed platforms, together with modeling, simulation, and experimentation directed toward optimization of operational concepts to employ this technology. The primary program focus is the advancement of capabilities for autonomous mobility through unstructured environments, concentrating on both perception and intelligent control technology. The program has developed the Experimental Unmanned vehicle (XUV), a small technology testbed vehicle. The XUV design couples multisensor perception with intelligent control to permit autonomous cross-country navigation at speeds of up to 32 kph during daylight and 16 kph during hours of darkness. The system design also encompasses onroad operations at up to 64 kph, including the capability to respond intelligently to other traffic and obstacles. When it concludes in 2002, Demo III will provide the military with both the technology and the initial experience required to develop and field the first generation of semiautonomous tactical ground vehicles for combat, combat support, and logistics applications.

The Demo III program has a high potential for technology spinoffs to civilian transportation, particularly in the area of obstacle detection and intelligent machine perception. This paper includes a description of the program approach, the sensor suite, progress in the last year; and potential spinoff areas to ITS.

Richard Bishop Consulting

General Dynamics Robotic Systems

Presented at the 11th ITS Annual Conference and Exposition, June 4-7, 2001 Miami Beach, Florida

This paper presents the methods and findings of a study focused on the preliminary development of a driver-vehicle interface (DVI) for a transit bus frontal and side collision avoidance system (CAS). A transit bus CAS is expected to function as a copilot, observing bus actions and intervening with various warnings and/or exerting a temporary, controlling function to avoid an otherwise imminent collision. The overall approach to the project involved tapping the expertise and experience of transit bus driving instructors and operators, and drawing on past passenger and commercial vehicle collision warning and avoidance system (CW/AS) research. Using these two sources of information, the transit bus operating environment was characterized. Then, preliminary CAS system requirements and functions were identified. Based on an understanding of the transit bus environment and CAS functionality, an initial set of transit bus CAS DVI display concepts was generated. A focus group with transit bus operators was conducted to review the initial display concepts. Based on focus group participant feedback, a final set of preliminary transit bus CAS DVI concepts was generated. Lastly, recommendations for future human factors testing and evaluation of the preliminary display concepts are proposed.

Foster-Miller, Inc.

Presented at the 11th ITS Annual Conference and Exposition, June 4-7, 2001 Miami Beach, Florida

This research is conducted for the purpose of detecting drowsiness/sleepiness of drivers for preventing traffic accidents on our highways. In this study we proposed a system which measures signals from the vehicle and then analyze those signals to detect drowsiness of driver. This method is based on an Artificial Neural Network (ANN). An ANN, with a hybrid architecture using an unsupervised clustering algorithm and a classifier, was trained and tested. Data used for training and testing was obtained from twelve drivers, each driving a simulator under different levels of sleep deprivation. We present a drowsiness detection method, which rely on signals from the vehicle only and thus present no obstruction to the driver.

The George Washington University Transportation Research Institute

Presented at the 11th ITS Annual Conference and Exposition, June 4-7, 2001 Miami Beach, Florida