Design of Driver-Assist Systems Under Probabilistic Safety Specifications Near Stop Signs

In this paper, we consider the problem of designing in-vehicle driver-assist systems that warn or override the driver to prevent collisions with a guaranteed probability. The probabilistic nature of the problem naturally arises from many sources of uncertainty, among which the behavior of the surrounding vehicles and the response of the driver to on-board warnings. We formulate this problem as a control problem for uncertain systems under probabilistic safety specifications and leverage the structure of the application domain to reach computationally efficient implementations. Simulations using a naturalistic data set show that the empirical probability of safety is always within 5% of the theoretical value in the case of direct driver override. In the case of on-board warnings, the empirical value is more conservative due primarily to drivers decelerating more strongly than requested. However, the empirical value is greater than or equal to the theoretical value, demonstrating a clear safety benefit

Automated mixed traffic vehicle control and scheduling study

The operation and the expected performance of a proposed automatic guideway transit system which uses low speed automated mixed traffic vehicles (AMTVs) were analyzed. Vehicle scheduling and headway control policies were evaluated with a transit system simulation model. The effect of mixed traffic interference on the average vehicle speed was examined with a vehicle pedestrian interface model. Control parameters regulating vehicle speed were evaluated for safe stopping and passenger comfort. Some preliminary data on the cost and operation of an experimental AMTV system are included. These data were the result of a separate task conducted at JPL, and were included as background information

Technology-Independent Algorithm for Collision Warning System at Semi-Controlled Intersections

Most of the collision warning systems that are available in the automotive market are mainly designed to detect imminent rear-end and/or lane-departure collisions. So far, no collision warning system is commercially available to detect imminent angle and turning collisions at semi-controlled intersections where the driver of a vehicle attempts to depart a minor road (controlled by a stop sign) to turn right, to turn left, or to cross an uncontrolled major road. One of the major causes for collisions at non-signalized intersections is the human error and misjudgment of the driver of the minor-road vehicle. Therefore, using a properly-designed collision warning system will have the potential to reduce, or even eliminate, this type of collision by reducing human error. This paper introduces a technology-independent algorithm for a collision warning system that can detect imminent collisions at semi-controlled intersections. The system utilizes commercially-available detectors to detect the approaching vehicles on the major road and calculate their speeds, accelerations, and rates of change of acceleration to estimate the time required to reach the intersection. The time required by the minor-road vehicle to clear the intersection is modeled as a function of driver and vehicle characteristics. By comparing the two times, the system displays a message for the driver of the minor-road vehicle when the departure maneuver is safe. An application example is provided to illustrate the proposed algorithm

Research on the System Safety Management in Urban Railway

Nowadays, rail transport has become one of the most widely utilised forms of transport thanks to its high safety level, large capacity, and cost-effectiveness. With the railway network's continuous development, including urban rail transit, one of the major areas of increasing attention and demand is ensuring safety or risk management in operation long-term remains for the whole life cycle by scientific tools, management of railway operation (Martani 2017), specifically in developed and developing countries like Vietnam. The situation in Vietnam demonstrates that the national mainline railway network has been built and operated entirely in a single narrow gauge (1000mm) since the previous century, with very few updates of manual operating technology. This significantly highlights that up to now, the conventional technique for managing the safety operation in general, and collision in particular, of the current Vietnamese railway system, including its subsystems, is only accident statistics which is not a scientific-based tool as the others like risk identify and analyse methods, risk mitigation…, that are already available in many countries. Accident management of Vietnam Railways is limited and responsible for accident statistics analysis to avoid and minimise the harm caused by phenomena that occur only after an accident. Statistical analysis of train accident case studies in Vietnam railway demonstrates that, because hazards and failures that could result in serious system occurrences (accidents and incidents) have not been identified, recorded, and evaluated to conduct safety-driven risk analysis using a well-suited assessment methodology, risk prevention and control cannot be achieved. Not only is it hard to forecast and avoid events, but it may also raise the chance and amount of danger, as well as the severity of the later effects. As a result, Vietnam's railway system has a high number of accidents and failure rates. For example, Vietnam Rail-ways' mainline network accounted for approximately 200 railway accidents in 2018, a 3% increase over the previous year, including 163 collisions between trains and road vehicles/persons, resulting in more than 100 fatalities and more than 150 casualties; 16 accidents, including almost derailments, the signal passed at danger… without fatality or casual-ty, but significant damage to rolling stock and track infrastructure (VR 2021). Focusing and developing a new standardised framework for safety management and availability of railway operation in Vietnam is required in view of the rapid development of rail urban transport in the country in recent years (VmoT 2016; VmoT 2018). UMRT Line HN2A in southwest Hanoi is the country's first elevated light rail transit line, which was completed and officially put into revenue service in November 2021. This greatly highlights that up to the current date, the UMRT Line HN2A is the first and only railway line in Vietnam with operational safety assessment launched for the first time and long-term remains for the whole life cycle. The fact that the UMRT Hanoi has a large capacity, more complicated rolling stock and infrastructure equipment, as well as a modern communica-tion-based train control (CBTC) signalling system and automatic train driving without the need for operator intervention (Lindqvist 2006), are all advantages. Developing a compatible and integrated safety management system (SMS) for adaption to the safety operating requirements of this UMRT is an important major point of concern, and this should be proven. In actuality, the system acceptance and safety certification phase for Metro Line HN2A prolonged up to 2.5 years owing to the identification of difficulties with noncompliance to safety requirements resulting from inadequate SMS documents and risk assessment. These faults and hazards have developed during the manufacturing and execution of the project; it is impossible to go back in time to correct them, and it is also impossible to ignore the project without assuming responsibility for its management. At the time of completion, the HN2A metro line will have required an expenditure of up to $868 million, thus it is vital to create measures to prevent system failure and assure passenger safety. This dissertation has reviewed the methods to solve the aforementioned challenges and presented a solution blueprint to attain the European standard level of system safety in three-phase as in the following: • Phase 1: applicable for lines that are currently in operation, such as Metro Line HN2A. Focused on operational and maintenance procedures, as well as a training plan for railway personnel, in order to enhance human performance. Complete and update the risk assessment framework for Metro Line HN2A. The dissertation's findings are described in these applications. • Phase 2: applicable for lines that are currently in construction and manufacturing, such as Metro Line HN3, Line HN2, HCMC Line 1 and Line 2. Continue refining and enhancing engineering management methods introduced during Phase 1. On the basis of the risk assessment by manufacturers (Line HN3, HCMC Line 2 with European manufacturers) and the risk assessment framework described in Chapter 4, a risk management plan for each line will be developed. Building Accident database for risk assessment research and development. • Phase 3: applicable for lines that are currently in planning. Enhance safety requirements and life-cycle management. Building a proactive Safety Culture step by step for the railway industry. This material is implemented gradually throughout all three phases, beginning with the creation of the concept and concluding with an improvement in the attitude of railway personnel on the HN2A line. In addition to this overview, Chapters 4 through Chapter 9 of the dissertation include particular solutions for Risk assessment, Vehicle and Infrastructure Maintenance methods, Inci-dent Management procedures, and Safety Culture installation. This document focuses on constructing a system safety concept for railway personnel, providing stringent and scientific management practises to assure proper engineering conditions, to manage effectively the metro line system, and ensuring passenger safety in Hanoi's metro operatio

Great East Japan Earthquake, JR East Mitigation Successes, and Lessons for California High-Speed Rail, MTI Report 12-37

California and Japan both experience frequent seismic activity, which is often damaging to infrastructure. Seismologists have developed systems for detecting and analyzing earthquakes in real-time. JR East has developed systems to mitigate the damage to their facilities and personnel, including an early earthquake detection system, retrofitting of existing facilities for seismic safety, development of more seismically resistant designs for new facilities, and earthquake response training and exercises for staff members. These systems demonstrated their value in the Great East Japan Earthquake of 2011 and have been further developed based on that experience. Researchers in California are developing an earthquake early warning system for the state, and the private sector has seismic sensors in place. These technologies could contribute to the safety of the California High-Speed Rail Authority’s developing system, which could emulate the best practices demonstrated in Japan in the construction of the Los Angeles-to-San Jose segment

Multisensor Data Fusion Strategies for Advanced Driver Assistance Systems

Multisensor data fusion and integration is a rapidly evolving research area that requires interdisciplinary knowledge in control theory, signal processing, artificial intelligence, probability and statistics, etc. Multisensor data fusion refers to the synergistic combination of sensory data from multiple sensors and related information to provide more reliable and accurate information than could be achieved using a single, independent sensor (Luo et al., 2007). Actually Multisensor data fusion is a multilevel, multifaceted process dealing with automatic detection, association, correlation, estimation, and combination of data from single and multiple information sources. The results of data fusion process help users make decisions in complicated scenarios. Integration of multiple sensor data was originally needed for military applications in ocean surveillance, air-to air and surface-to-air defence, or battlefield intelligence. More recently, multisensor data fusion has also included the nonmilitary fields of remote environmental sensing, medical diagnosis, automated monitoring of equipment, robotics, and automotive systems (Macci et al., 2008). The potential advantages of multisensor fusion and integration are redundancy, complementarity, timeliness, and cost of the information. The integration or fusion of redundant information can reduce overall uncertainty and thus serve to increase the accuracy with which the features are perceived by the system. Multiple sensors providing redundant information can also serve to increase reliability in the case of sensor error or failure. Complementary information from multiple sensors allows features in the environment to be perceived that are impossible to perceive using just the information from each individual sensor operating separately. (Luo et al., 2007) Besides, driving as one of our daily activities is a complex task involving a great amount of interaction between driver and vehicle. Drivers regularly share their attention among operating the vehicle, monitoring traffic and nearby obstacles, and performing secondary tasks such as conversing, adjusting comfort settings (e.g. temperature, radio.) The complexity of the task and uncertainty of the driving environment make driving a very dangerous task, as according to a study in the European member states, there are more than 1,200,000 traffic accidents a year with over 40,000 fatalities. This fact points up the growing demand for automotive safety systems, which aim for a significant contribution to the overall road safety (Tatschke et al., 2006). Therefore, recently, there are an increased number of research activities focusing on the Driver Assistance System (DAS) development in order O pe n A cc es s D at ab as e w w w .in te ch w eb .o r

Real-time sensor data development for smart truck drivetrains

Heavy articulated transport vehicles have a poor reputation associated with dramatic road accidents with frequent fatalities for those in automobiles. The result of this work is a formal data flow structure to enhance real-time decision-making in complex mechanical systems to increase performance capability and responsiveness to human commands. This structure recognizes the multiple layers of highly non-linear mechanical components (actuators, wheel tire & ground surfaces, controllers, power supplies, human/machine interfaces, etc.) that must operate in unison (i.e., reduce conflicts) in real-time (in milli-seconds) to enhance operator (driver) control to maximize human choice. This work contains a discussion on dependable sensor data is vital in complex systems that rely on a suite of sensors for both control as well as condition monitoring purposes as well as discussion on real-time energy distribution analysis in high momentum mechanical systems. The focus will be on tractor trucks of class 7 & 8 that are outfitted with an array of low-cost redundant sensors leveraging advances in intelligent robotic systems. This work details many topics including: Most relevant sensor types and their technologies, Designing, implementing, and maintaining a multi-sensor system using feasible industry standards, Sensor signal integrity and data flow processing for decision making, Asynchronous data flow methods for operating decision making schemes in real-time, Multiple applications to enhance tractor trucks systems with multi-sensor systems for real-time decision making.Mechanical Engineerin