Technology transfer: Transportation

The application of NASA derived technology in solving problems related to highways, railroads, and other rapid systems is described. Additional areas/are identified where space technology may be utilized to meet requirements related to waterways, law enforcement agencies, and the trucking and recreational vehicle industries

Technology transfer: Transportation

The successful application of aerospace technology to problems related to highways and rail and rapid transit systems is described with emphasis on the use of corrosion resistant paints, fire retardant materials, and law enforcement. Possible areas for the use of spinoff from NASA technology by the California State Department of Corrections are identified. These include drug detection, security and warning systems, and the transportation and storage of food. A communication system for emergency services is also described

Transportation, Terrorism and Crime: Deterrence, Disruption and Resilience

Abstract: Terrorists likely have adopted vehicle ramming as a tactic because it can be carried out by an individual (or “lone wolf terrorist”), and because the skills required are minimal (e.g. the ability to drive a car and determine locations for creating maximum carnage). Studies of terrorist activities against transportation assets have been conducted to help law enforcement agencies prepare their communities, create mitigation measures, conduct effective surveillance and respond quickly to attacks. This study reviews current research on terrorist tactics against transportation assets, with an emphasis on vehicle ramming attacks. It evaluates some of the current attack strategies, and the possible mitigation or response tactics that may be effective in deterring attacks or saving lives in the event of an attack. It includes case studies that can be used as educational tools for understanding terrorist methodologies, as well as ordinary emergencies that might become a terrorist’s blueprint

Probabilistic Lane Association

Lane association is the problem of determining in which lane a vehicle is currently driving, which is of interest for automated driving where the vehicle must understand its surroundings. Limited to highway scenarios, a method combining data from different sensors to extract information about the currently associated lane is presented. The suggested method splits the problem in two main parts, lane change identification and road edge detection. The lane change identification mainly uses information from the camera to model the lateral movement on the road and identifies the lane changes as a relative position on the road. This part is implemented with a particle filter. The road edge detection enters radar detections to an iterated Kalman filter and estimates the distances to the road edges. Finally, a combination of the filter outputs makes it possible to compute an absolute position on the road. Comparing the relative and absolute positioning then leads to the desired lane association estimate. The results produced are reliable and encourages to continue approaching this problem in a similar manner, but the current implementation is computationally heavy

DALL-E Does Palsgraf

What happens when we ask a leading artificial intelligence (AI) tool for image generation to illustrate the facts of a leading law school case? This article does just that. I first introduce this tool specifically and machine learning generally. I then summarize the seminal case of Palsgraf v. Long Island Railroad. For the main event, I show the images that the tool created based on the facts as the majority and dissent recount them. Finally, I translate this exercise into lessons for how lawyers and the law should think about AI

Potential Terrorist Uses of Highway-Borne Hazardous Materials, MTI Report 09-03

The Department of Homeland Security (DHS) has requested that the Mineta Transportation Institutes National Transportation Security Center of Excellence (MTI NTSCOE) provide any research it has or insights it can provide on the security risks created by the highway transportation of hazardous materials. This request was submitted to MTI/NSTC as a National Transportation Security Center of Excellence. In response, MTI/NTSC reviewed and revised research performed in 2007 and 2008 and assembled a small team of terrorism and emergency-response experts, led by Center Director Brian Michael Jenkins, to report on the risks of terrorists using highway shipments of flammable liquids (e.g., gasoline tankers) to cause casualties anywhere, and ways to reduce those risks. This report has been provided to DHS. The teams first focus was on surface transportation targets, including highway infrastructure, and also public transportation stations. As a full understanding of these materials, and their use against various targets became revealed, the team shifted with urgency to the far more plentiful targets outside of surface transportation where people gather and can be killed or injured. However, the team is concerned to return to the top of the use of these materials against public transit stations and recommends it as a separate subject for urgent research

Rail Passenger Selective Screening Summit, MTI S-09-01

This publication is an edited transcript of the Rail Passenger Selective Screening Summit, which was co-sponsored by MTI and the American Public Transportation Association (APTA) in Chicago, Illinois on June 18, 2009, during APTA´s annual Rail Conference. The workshop was moderated by Brian Michael Jenkins, director, Mineta Transportation Institute\u27s National Transportation Security Center of Excellence (NTSCOE). Speakers included Bruce R. Butterworth, co-author, Selective Screening of Rail Passengers; Greg Hull, president, American Public Transportation Association (APTA); Paul MacMillan, chief of police, Massachusetts Bay Transportation Authority, Transit Police Department; Ron Masciana, deputy chief, Metropolitan Transit Authority (MTA), New York; Jesus Ojeda, security coordinator, Southern California Regional Rail Authority; Ed Phillips, operations deputy, Office of Security, Amtrak; and John P. Sammon, assistant administrator, Transportation Sector Network Management, Transportation Security Administration (TSA

Resilient Multi-range Radar Detection System for Autonomous Vehicles: A New Statistical Method

© 2023 Crown. This is an open access article distributed under the terms of the Creative Commons Attribution License (CC BY), https://creativecommons.org/licenses/by/4.0/Critical issues with current detection systems are their susceptibility to adverse weather conditions and constraint on the vertical field view of the radars limiting the ability of such systems to accurately detect the height of the targets. In this paper, a novel multi-range radar (MRR) arrangement (i.e. triple: long-range, medium-range, and short-range radars) based on the sensor fusion technique is investigated that can detect objects of different sizes in a level 2 advanced driver-assistance system. To improve the accuracy of the detection system, the resilience of the MRR approach is investigated using the Monte Carlo (MC) method for the first time. By adopting MC framework, this study shows that only a handful of fine-scaled computations are required to accurately predict statistics of the radar detection failure, compared to many expensive trials. The results presented huge computational gains for such a complex problem. The MRR approach improved the detection reliability with an increased mean detection distance (4.9% over medium range and 13% over long range radar) and reduced standard deviation over existing methods (30% over medium range and 15% over long-range radar). This will help establishing a new path toward faster and cheaper development of modern vehicle detection systems.Peer reviewe

2009 Regional Safety Action Plan: Improving Transportation Safety in the Delaware Valley

Over 450 people die in crashes on the roads of the nine-county Delaware Valley in an average year. Over 50,000 people are injured in approximately 90,000 crashes. The Safety Action Plan includes a methodology to define key safety emphasis areas, a range of strategies for each of the seven emphasis areas, and a focused implementation table to reduce the number of fatalities. The Safety Action Plan was developed with guidance from the multi-disciplinary Regional Safety Task Force. The implementation table is an agreed-upon starting point for how partners will work together to improve traffic safety in the region. Each meeting of the Regional Safety Task Force will include reporting back on progress on the implementation table. The Safety Action Plan will be updated regularly. Analysis of crash data for the region is provided in Traffic Crash Analysis of the Delaware Valley (Publication Number 08054)