Methodology to assess safety effects of future Intelligent Transport Systems on railway level crossings

There is consistent evidence showing that driver behaviour contributes to crashes and near miss incidents at railway level crossings (RLXs). The development of emerging Vehicle-to-Vehicle and Vehicle-to-Infrastructure technologies is a highly promising approach to improve RLX safety. To date, research has not evaluated comprehensively the potential effects of such technologies on driving behaviour at RLXs. This paper presents an on-going research programme assessing the impacts of such new technologies on human factors and drivers’ situational awareness at RLX. Additionally, requirements for the design of such promising technologies and ways to display safety information to drivers were systematically reviewed. Finally, a methodology which comprehensively assesses the effects of in-vehicle and road-based interventions warning the driver of incoming trains at RLXs is discussed, with a focus on both benefits and potential negative behavioural adaptations. The methodology is designed for implementation in a driving simulator and covers compliance, control of the vehicle, distraction, mental workload and drivers’ acceptance. This study has the potential to provide a broad understanding of the effects of deploying new in-vehicle and road-based technologies at RLXs and hence inform policy makers on safety improvements planning for RLX

Vision-Based Lane-Changing Behavior Detection Using Deep Residual Neural Network

Accurate lane localization and lane change detection are crucial in advanced driver assistance systems and autonomous driving systems for safer and more efficient trajectory planning. Conventional localization devices such as Global Positioning System only provide road-level resolution for car navigation, which is incompetent to assist in lane-level decision making. The state of art technique for lane localization is to use Light Detection and Ranging sensors to correct the global localization error and achieve centimeter-level accuracy, but the real-time implementation and popularization for LiDAR is still limited by its computational burden and current cost. As a cost-effective alternative, vision-based lane change detection has been highly regarded for affordable autonomous vehicles to support lane-level localization. A deep learning-based computer vision system is developed to detect the lane change behavior using the images captured by a front-view camera mounted on the vehicle and data from the inertial measurement unit for highway driving. Testing results on real-world driving data have shown that the proposed method is robust with real-time working ability and could achieve around 87% lane change detection accuracy. Compared to the average human reaction to visual stimuli, the proposed computer vision system works 9 times faster, which makes it capable of helping make life-saving decisions in time

VANET Applications: Hot Use Cases

Current challenges of car manufacturers are to make roads safe, to achieve free flowing traffic with few congestions, and to reduce pollution by an effective fuel use. To reach these goals, many improvements are performed in-car, but more and more approaches rely on connected cars with communication capabilities between cars, with an infrastructure, or with IoT devices. Monitoring and coordinating vehicles allow then to compute intelligent ways of transportation. Connected cars have introduced a new way of thinking cars - not only as a mean for a driver to go from A to B, but as smart cars - a user extension like the smartphone today. In this report, we introduce concepts and specific vocabulary in order to classify current innovations or ideas on the emerging topic of smart car. We present a graphical categorization showing this evolution in function of the societal evolution. Different perspectives are adopted: a vehicle-centric view, a vehicle-network view, and a user-centric view; described by simple and complex use-cases and illustrated by a list of emerging and current projects from the academic and industrial worlds. We identified an empty space in innovation between the user and his car: paradoxically even if they are both in interaction, they are separated through different application uses. Future challenge is to interlace social concerns of the user within an intelligent and efficient driving

Intelligent Transportation Systems Strategic Plan (Phase I Report)

This interim report on an Intelligent Transportation Systems Strategic (ITS) Plan has been developed as documentation of the process of offering a vision for ITS and recommending an outline for organizational structure, infrastructure, and long-term planning for ITS in Kentucky. This plan provides an overview of the broad scope of ITS and relationships between various Intelligent Vehicle Highway Systems (IVHS) functional areas and ITS user service areas. Three of the functional areas of ITS have been addressed in this interim report with sections devoted to mission, vision, goals, and potential technology applications. Within each of the three areas, recommendations have been made for applications and technologies for deployment. A more formalized business plan for will be developed to recommend specific projects for implementation. Those three functional areas are 1) Advanced Rural Transportation Systems (ARTS), 2) Advanced Traveler Information Systems (ATIS), and 3) Commercial Vehicle Operations (CVO). A survey of other states was conducted to determine the status of the development of ITS strategic plans. Information received from the 11 states that had completed strategic plans was used to determine the overall approach taken in development of the plans and to evaluate the essential contents of the reports for application in Kentucky. Kentucky\u27s ITS Strategic Plan evolved from an early decision by representatives of the Kentucky Transportation Cabinet (KyTC) to formalize the procedure by requesting the Kentucky Transportation Center to prepare a work plan outlining the proposed tasks. Following several introductory meetings of the Study Advisory Committee, additional focus group meetings were held with various transportation representatives to identify ITS issues of importance. Results from these meetings were compiled and used as input to the planning process for development of the Strategic Plan components of ARTS and ATIS. The development of a strategic plan for Commercial Vehicle Operations originated from a different procedure than did the other functional areas of ITS. As part of well-developed commercial vehicle activities through the ITS-related programs of Advantage I-75 and CVISN, Kentucky has become a national leader in this area and has developed a strategic plan of advanced technology applications to commercial vehicles. The strategic plan for Commercial Vehicle Operations was developed out of the convergence of several parallel processes in Kentucky. Empower Kentucky work teams had met over a two-year period to develop improved and more efficient processes for CVO in Kentucky. Their conclusions and recommendations encouraged the further activities of the Kentucky ITS/CVO working group that first convened in the summer of 1996. In an effort to conceptually organize the various ITS/CVO activities in Kentucky, and as a commitment to the CVISN Mainstreaming plan, an inclusive visioning exercise was held in early 1997. Out of this exercise emerged the six critical vision elements that guided the CVO strategic plan. The remaining functional areas to be included in the ITS Strategic Plan will be addressed in the second phase of this study. Those areas are Advanced Traffic Management Systems (ATMS), Advanced Vehicle Control Systems (AVCS), and Advanced Public Transportation Systems (APTS). It is anticipated that a process similar to that developed for the first phase of this study will continue