Integrating Multiple Alarms & Driver Situation Awareness

This study addresses this gap in CAS and intelligent alarm research by examining whether or not a single master alarm warning versus multiple warnings for the different collision warning systems conveys adequate information to the drivers. Intelligent driver warning systems signaling impending frontal and rear collisions, as well as unintentional lane departures were used in this experiment, and all the warnings were presented to drivers through the auditory channel only. We investigated two critical research questions in this study: 1. Do multiple intelligent alarms as opposed to a single master alarm affect drivers’ recognition, performance, and action when they experience a likely imminent collision and unintentional lane departure? 2. Is driver performance and overall situation awareness under the two different alarm alerting schemes affected by reliabilities of the warning systems?Prepared For Ford Motor Compan

Multiple Alarms and Driving Situational Awareness

There is increasing interest in actively mitigating safety in vehicles beyond that of improving crash worthiness. According to the National Highway Transportation Safety Administration (NHTSA), there are more than 40,000 deaths on highways each year. This number may be decreasing with increasing active public concern and awareness for the use of safety restraints, but the numbers are still in excess of 40,000 deaths annually. Focusing on crash-worthiness as a measure of safety in vehicles will eventually reach a point of diminishing return, thus there is a need for automotive manufacturers to shift their safety focus to crash avoidance safety systems (Runge, 2002). In the public domain, significant progress and advancements have been made under the Intelligent Vehicles Initiative (IVI) set up by U.S. Department of Transportation to prevent motor vehicle crashes by assisting drivers in avoiding hazardous mistakes (U.S DOT, 1998). One IVI focus area is facilitating the rapid deployment of Collision Avoidance Systems (CAS) in vehicles. Collision Avoidance Systems are a subset of Advanced Vehicle Control Safety Systems (AVCSS) which come under the umbrella of Intelligent Transportation Systems (ITS). These Collision Avoidance Systems warn drivers of imminent collisions and can potentially help to save lives. Primary directions of research in CAS are determining implementation strategies and technologies in vehicles and roadway infrastructure, as well as optimizing the driving performance of different populations of drivers when using CAS. In CAS implementation, vehicles will communicate with other vehicles as well as with the roadway infrastructure via sensors and telecommunication networks. The data obtained can then be used in Collision Avoidance Systems. Vehicle-to-vehicle CAS include warnings that trigger when a vehicle is about to collide with another vehicle. Examples include Frontal Warning, Rear Warning and Blind Spot Detection Warnings. Vehicle-to-infrastructure CAS include warnings that trigger when a vehicle is about to have a collision with the roadway infrastructure. Examples include Intersection Warnings, Lane Departure Warnings, Curve Speed Warnings and Road-condition Warnings. Driving in a dynamic environment has become increasingly complex, such that drivers must visually track objects, monitor a constantly changing system, manage system information, to include the explosion of telematics, and make decisions in this dynamic and potentially high mental workload environment. Introducing Collision Avoidance Systems into vehicles could add to the complexity of this dynamic environment as different drivers will respond differently to Collision Avoidance Systems and there are many critical human factors issues that require investigation.Prepared for Ford Motor Co

A high speed Tri-Vision system for automotive applications

Purpose: Cameras are excellent ways of non-invasively monitoring the interior and exterior of vehicles. In particular, high speed stereovision and multivision systems are important for transport applications such as driver eye tracking or collision avoidance. This paper addresses the synchronisation problem which arises when multivision camera systems are used to capture the high speed motion common in such applications. Methods: An experimental, high-speed tri-vision camera system intended for real-time driver eye-blink and saccade measurement was designed, developed, implemented and tested using prototype, ultra-high dynamic range, automotive-grade image sensors specifically developed by E2V (formerly Atmel) Grenoble SA as part of the European FP6 project – sensation (advanced sensor development for attention stress, vigilance and sleep/wakefulness monitoring). Results : The developed system can sustain frame rates of 59.8 Hz at the full stereovision resolution of 1280 × 480 but this can reach 750 Hz when a 10 k pixel Region of Interest (ROI) is used, with a maximum global shutter speed of 1/48000 s and a shutter efficiency of 99.7%. The data can be reliably transmitted uncompressed over standard copper Camera-Link® cables over 5 metres. The synchronisation error between the left and right stereo images is less than 100 ps and this has been verified both electrically and optically. Synchronisation is automatically established at boot-up and maintained during resolution changes. A third camera in the set can be configured independently. The dynamic range of the 10bit sensors exceeds 123 dB with a spectral sensitivity extending well into the infra-red range. Conclusion: The system was subjected to a comprehensive testing protocol, which confirms that the salient requirements for the driver monitoring application are adequately met and in some respects, exceeded. The synchronisation technique presented may also benefit several other automotive stereovision applications including near and far-field obstacle detection and collision avoidance, road condition monitoring and others.Partially funded by the EU FP6 through the IST-507231 SENSATION project.peer-reviewe

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

Automated driving and autonomous functions on road vehicles

In recent years, road vehicle automation has become an important and popular topic for research and development in both academic and industrial spheres. New developments received extensive coverage in the popular press, and it may be said that the topic has captured the public imagination. Indeed, the topic has generated interest across a wide range of academic, industry and governmental communities, well beyond vehicle engineering; these include computer science, transportation, urban planning, legal, social science and psychology. While this follows a similar surge of interest – and subsequent hiatus – of Automated Highway Systems in the 1990’s, the current level of interest is substantially greater, and current expectations are high. It is common to frame the new technologies under the banner of “self-driving cars” – robotic systems potentially taking over the entire role of the human driver, a capability that does not fully exist at present. However, this single vision leads one to ignore the existing range of automated systems that are both feasible and useful. Recent developments are underpinned by substantial and long-term trends in “computerisation” of the automobile, with developments in sensors, actuators and control technologies to spur the new developments in both industry and academia. In this paper we review the evolution of the intelligent vehicle and the supporting technologies with a focus on the progress and key challenges for vehicle system dynamics. A number of relevant themes around driving automation are explored in this article, with special focus on those most relevant to the underlying vehicle system dynamics. One conclusion is that increased precision is needed in sensing and controlling vehicle motions, a trend that can mimic that of the aerospace industry, and similarly benefit from increased use of redundant by-wire actuators

Modelling mixed traffic flow of autonomous vehicles and human-driven vehicles

Autonomous Vehicles (AVs) are bringing revolutionary opportunities and challenges to urban transport systems. They can reduce congestion, improve operational efficiency and liberate drivers from driving. Though AVs might bring attractive potential benefits, most benefits are evaluated at high AV penetration rates or an all-AV scenario. In practice, limited by price barriers, adoption rates and vehicle-renewal periods, AVs may not replace Human-Driven Vehicles (HDVs) to achieve a high penetration rate in a short time. It can be expected that the road network will operate with a mix of AVs and HDVs in the near to medium future. Therefore, there is a strong motivation to analyse the performance of road networks under mixed traffic conditions. The overall aim of this PhD research is to analyse mixed traffic flows of AVs and HDVs to help traffic managers and Local Authorities (LAs) improve the performance of urban traffic systems by right-of-way reallocation and dynamic traffic management. To achieve this aim, this PhD research is divided into four parts. Firstly, the impact of heterogeneity between AVs and HDVs on road capacity is investigated. A theoretical model is proposed to calculate the maximum capacity of heterogeneous traffic flow. Based on the theoretical model, it is shown that road capacity increases convexly with AV penetration rates. This finding provides a theoretical basis to support the hypothesis that right-of-way reallocation can increase road capacity under the mixed traffic flow. To cross-validate the above finding, different right-of-way reallocation strategies are evaluated on a two-lane road with SUMO simulation. Compared with a do-nothing scenario, the road capacity can be increased by approximately 11% with a proper RoW reallocation strategy at low or medium AV penetration rates. Secondly, whether CAVs can be used as mobile traffic controllers by adjusting their speed on a certain link is investigated. It is found that in some circumstances, system efficiency can be improved by CAVs adjusting their speed on a certain link to nudge the network towards the system optimum. According to a numerical analysis on the Braess network, total travel time can be reduced by 9.7% when CAVs actively slow down on a link. To take more realistic circumstances into account, a SUMO simulation case study is conducted, where HDVs only have partial knowledge about travel costs. The results of the simulation demonstrate that when CAVs are acting as mobile traffic controllers by actively reducing speed on a certain link, total travel time can be reduced by approximately 6.8% compared with the do-nothing scenario. Thirdly, whether travel efficiency can be improved with only a part of the vehicular flow cooperatively changing their routing under mixed conditions is investigated. It has been found that it is possible to use CAVs to influence HDVs’ day-to-day routing and push the network towards the system optimal distribution dynamically on a large network with multiple OD pairs. Taking non-linear cost-flow relationship and signal timing into account, an Optimal Routing and Signal Timing (ORST) control strategy is proposed for CAVs and tested in simulation. Compared with initial user equilibrium, total travel time can be reduced by approximately 7% when a portion of CAVs cooperatively charge their routing with the ORST control strategy at the 75% CAV penetration rate. This opens up possibilities, besides road pricing, to improve system efficiency by controlling routing and signal timing strategy for CAVs. Fourthly, whether additional travel efficiency can be achieved by jointly optimising routing and signal timing with information from CAVs is further investigated. Specifically, the impact of information levels on routing and signal timing efficiency has been investigated quantitatively. The results demonstrate that different levels of information will lead the road traffic system to reach different equilibrium points. Then the proposed ORST control strategy is compared with existing routing and signal timing strategies. The results present that ORST can reduce approximately 10% of the total travel time compared to user equilibrium. In addition, the proposed model has also been tested on a revised Nguyen-Dupuis network. At 25% CAV penetration rates, the proposed model can successfully reduce approximately 23% of total travel time. In summary, the mixed flow of AVs and HDVs is investigated in this PhD research. To increase the efficiency of urban traffic systems, novel strategies have been proposed and tested with numerical analysis and simulation, which provides inspirations and quantitative evidence for traffic managers and LAs to manage the mixed traffic flow efficiently.Open Acces

INTELLIGENTE TRANSPORT SYSTEMEN ITS EN VERKEERSVEILIGHEID

This report discusses Intelligent Transport Systems (ITS). This generic term is used for a broad range of information-, control- and electronic technology that can be integrated in the road infrastructure and the vehicles themselves, saving lives, time and money bymonitoring and managing traffic flows, reducing conges-tion, avoiding accidents, etc. Because this report was written in the scope of the Policy Research Centre Mobility & Public Works, track Traffic Safety, it focuses on ITS systems from the traffic safety point of view. Within the whole range of ITS systems, two categories can be distinguished: autonomous and cooperative systems. Autonomous systems are all forms of ITS which operate by itself, and do not depend on the cooperation with other vehicles or supporting infrastructure. Example applications are blind spot detection using radar, electronic stability control, dynamic traffic management using variable road signs, emergency call, etc. Cooperative systems are ITS systems based on communication and cooperation, both between vehicles as between vehicles and infrastructure. Example applications are alerting vehicles approaching a traffic jam, exchanging data regarding hazardous road conditions, extended electronic brake light, etc. In some cases, autonomous systems can evolve to autonomous cooperative systems. ISA (Intelligent Speed Adaptation) is an example of this: the dynamic aspect as well as communication with infrastructure (eg Traffic lights, Variable Message Sign (VMS)...) can provide additional road safety. This is the clear link between the two parts of this report. The many ITS applications are an indicator of the high expectations from the government, the academic world and the industry regarding the possibilities made possible by both categories of ITS systems. Therefore, the comprehensive discussion of both of them is the core of this report. The first part of the report covering the autonomous systems treats two aspects: 1. Overview of European projects related to mobility and in particular to road safety 2. Overview for guidelines for the evaluation of ITS projects. Out of the wide range of diverse (autonomous) ITS applications a selection is made; this selection is focused on E Safety Forum and PreVENT. Especially the PreVent research project is interesting because ITS-applications have led to a number of concrete demonstration vehicles that showed - in protected and unprotected surroundings- that these ITS-applications are already technically useful or could be developed into useful products. The component “guidelines for the evaluation of ITS projects” outlines that the government has to have specific evaluation tools if the government has the ambition of using ITS-applications for road safety. Two projects -guidelines for the evaluation of ITS projects- are examined; a third evaluation method is only mentioned because this description shows that a specific targeting of the government can be desirable : 1. TRACE describes the guidelines for the evaluation of ITS projects which are useful for the evaluation of specific ITS-applications. 2. FITS contains Finnish guidelines for the evaluation of ITS project; FIS is an adaptation of methods used for evaluation of transport projects. 3. The third evaluation method for the evaluation of ITS projects is developed in an ongoing European research project, eImpact. eImpact is important because, a specific consultation of stake holders shows that the social importance of some techniques is underestimated. These preliminary results show that an appropriate guiding role for the government could be important. In the second part of this document the cooperative systems are discussed in depth. These systems enable a large number of applications with an important social relevance, both on the level of the environment, mobility and traffic safety. Cooperative systems make it possible to warn drivers in time to avoid collisions (e.g. when approaching the tail of a traffic jam, or when a ghost driver is detected). Hazardous road conditions can be automatically communicated to other drivers (e.g. after the detection of black ice or an oil trail by the ESP). Navigation systems can receive detailed real-time up-dates about the current traffic situation and can take this into account when calculating their routes. When a traffic distortion occurs, traffic centers can immediately take action and can actively influence the way that the traffic will be diverted. Drivers can be notified well in advance about approaching emergency vehicles, and can be directed to yield way in a uniform manner. This is just a small selection from the large number of applications that are made possible because of cooperative ITS systems, but it is very obvious that these systems can make a significant positive contribution to traffic safety. In literature it is estimated that the decrease of accidents with injuries of fatalities will be between 20% and 50% . It is not suprising that ITS systems receive a lot of attention for the moment. On an international level, a number of standards are being established regarding this topic. The International Telecommunications Uniont (ITU), Institute for Electrical and Electronics Engineers (IEEE), International Organization for Standardization (ISO), Association of Radio Industries and Business (ARIB) and European committee for standardization (CEN) are currently defining standards that describe different aspects of ITS systems. One of the names that is mostly mentioned in literature is the ISO TC204/WG16 Communications Architecture for Land Mobile environment (CALM) standard. It describes a framework that enables transparent (both for the application and the user) continuous communication through different communication media. Besides the innumerable standardization activities, there is a great number of active research projects. On European level, the most important are the i2010 Intelligent Car Initiative, the eSafety Forum, and the COMeSafety, the CVIS, the SAFESPOT, the COOPERS and the SEVECOM project. The i2010 Intelligent Car Initiative is an European initiative with the goal to halve the number of traffic casualties by 2010. The eSafety Forum is an initiative of the European Commission, industry and other stakeholders and targets the acceleration of development and deployment of safety-related ITS systems. The COMeSafety project supports the eSafety Forum on the field of vehicle-to-vehicle and vehicle-to-infrastructure communication. In the CVIS project, attention is given to both technical and non-technical issues, with the main goal to develop the first free and open reference implementation of the CALM architecture. The SAFEST project investigates which data is important for safety applications, and with which algorithmsthis data can be extracted from vehicles and infrastructure. The COOPERS project mainly targets communication between vehicles and dedicated roadside infrastructure. Finally, the SEVECOM project researches security and privacy issues. Besides the European projects, research is also conducted in the United States of America (CICAS and VII projects) and in Japan (AHSRA, VICS, Smartway, internetITS). Besides standardization bodies and governmental organizations, also the industry has a considerable interest in ITS systems. In the scope of their ITS activities, a number of companies are united in national and international organizations. On an international level, the best known names are the Car 2 Car Communication Consortium, and Ertico. The C2C CC unites the large European car manufacturers, and focuses on the development of an open standard for vehicle-to-vehicle and vehicle-to-infrastructure communications based on the already well established IEEE 802.11 WLAN standard. Ertico is an European multi-sector, public/private partnership with the intended purpose of the development and introduction of ITS systems. On a national level, FlandersDrive and The Telematics Cluster / ITS Belgium are the best known organizations. Despite the worldwide activities regarding (cooperative) ITS systems, there still is no consensus about the wireless technology to be used in such systems. This can be put down to the fact that a large number of suitable technologies exist or are under development. Each technology has its specific advantages and disadvantages, but no single technology is the ideal solution for every ITS application. However, the different candidates can be classified in three distinct categories. The first group contains solutions for Dedicated Short Range Communication (DSRC), such as the WAVE technology. The second group is made up of several cellular communication networks providing coverage over wide areas. Examples are GPRS (data communication using the GSM network), UMTS (faster then GPRS), WiMAX (even faster then UMTS) and MBWA (similar to WiMAX). The third group consists of digital data broadcast technologies such as RDS (via the current FM radio transmissions, slow), DAB and DMB (via current digital radio transmissions, quicker) and DVB-H (via future digital television transmissions for mobiledevices, quickest). The previous makes it clear that ITS systems are a hot topic right now, and they receive a lot of attention from the academic world, the standardization bodies and the industry. Therefore, it seems like that it is just a matter of time before ITS systems will find their way into the daily live. Due to the large number of suitable technologies for the implementation of cooperative ITS systems, it is very hard to define which role the government has to play in these developments, and which are the next steps to take. These issues were addressed in reports produced by the i2010 Intelligent Car Initiative and the CVIS project. Their state of the art overview revealed that until now, no country has successfully deployed a fully operational ITS system yet. Seven EU countries are the furthest and are already in the deployment phase: Sweden, Germany, the Netherlands, the United Kingdom, Finland, Spain and France. These countries are trailed by eight countries which are in the promotion phase: Denmark, Greece, Italy, Austria, Belgium,Norway, the Czech Republic and Poland. Finally, the last ten countries find themselves in the start-up phase: Estonia, Lithuania, Latvia, Slovenia, Slovakia, Hungary, Portugal, Switzerland, Ireland and Luxembourg. These European reports produced by the i2010 Intelligent Car Initiative and the CVIS project have defined a few policy recommendations which are very relevant for the Belgian and Flemish government. The most important recommendations for the Flemish government are: • Support awareness: research revealed that civilians consider ITS applications useful, but they are not really willing to pay for this technology. Therefore, it is important to convince the general public of the usefulness and the importance of ITS systems. • Fill the gaps: Belgium is situated in the promotion phase. This means that it should focus at identifying the missing stakeholders, and coordinating national and regional ITS activities. Here it is important that the research activities are coordinated in a national and international context to allow transfer of knowledge from one study to the next, as well as the results to be comparable. • Develop a vision: in the scope of ITS systems policies have to be defined regarding a large number of issues. For instance there is the question if ITS users should be educated, meaning that the use of ITS systems should be the subject of the drivers license exam. How will the regulations be for the technical inspection of vehicles equipped with ITS technology? Will ITS systems be deployed on a voluntary base, or will they e.g. be obliged in every new car? Will the services be offered by private companies, by the public authorities, or by a combination of them? Which technology will be used to implement ITS systems? These are just a few of the many questions where the government will have to develop a point of view for. • Policy coordination: ITS systems are a policy subject on an international, national and regional level. It is very important that these policy organizations can collaborate in a coordinated manner. • Iterative approach to policy development: developing policies for this complex matter is not a simple task. This asks for an iterative approach, where policy decisions are continuously refined and adjusted

Risk analysis of autonomous vehicle and its safety impact on mixed traffic stream

In 2016, more than 35,000 people died in traffic crashes, and human error was the reason for 94% of these deaths. Researchers and automobile companies are testing autonomous vehicles in mixed traffic streams to eliminate human error by removing the human driver behind the steering wheel. However, recent autonomous vehicle crashes while testing indicate the necessity for a more thorough risk analysis. The objectives of this study were (1) to perform a risk analysis of autonomous vehicles and (2) to evaluate the safety impact of these vehicles in a mixed traffic stream. The overall research was divided into two phases: (1) risk analysis and (2) simulation of autonomous vehicles. Risk analysis of autonomous vehicles was conducted using the fault tree method. Based on failure probabilities of system components, two fault tree models were developed and combined to predict overall system reliability. It was found that an autonomous vehicle system could fail 158 times per one-million miles of travel due to either malfunction in vehicular components or disruption from infrastructure components. The second phase of this research was the simulation of an autonomous vehicle, where change in crash frequency after autonomous vehicle deployment in a mixed traffic stream was assessed. It was found that average travel time could be reduced by about 50%, and 74% of conflicts, i.e., traffic crashes, could be avoided by replacing 90% of the human drivers with autonomous vehicles

Behavioural adaptation and acceptance

One purpose of Intelligent Vehicles is to improve road safety, throughput, and emissions. However, the predicted effects are not always as large as aimed for. Part of this is due to indirect behavioral changes of drivers, also called behavioral adaptation. Behavioral adaptation (BA) refers to unintended behavior that arises following a change to the road traffic system. Qualitative models of behavioral adaptation (formerly known as risk compensation) describe BA by the change in the subjectively perceived enhancement of the safety margins. If a driver thinks that the system is able to enhance safety and also perceives the change in behavior as advantageous, adaptation occurs. The amount of adaptation is (indirectly) influenced by the driver personality and trust in the system. This also means that the amount of adaptation differs between user groups and evenwithin one driver or changes over time. Examples of behavioral change are the generation of extra mobility (e.g., taking the car instead of the train), road use by ‘‘less qualified’’ drivers (e.g., novice drivers), driving under more difficult conditions (e.g., driving on slippery roads), or a change in distance to the vehicle ahead (e.g., driving closer to a lead vehicle with ABS). In effect predictions, behavioral adaptation should be taken into account. Even though it may reduce beneficial effects, BA (normally) does not eliminate the positive effects. How much the effects are reduced depends on the type of ADAS, the design of the ADAS, the driver, the current state of the driver, and the local traffic and weather conditions

Evaluation of the Driving Performance and User Acceptance of a Predictive Eco-Driving Assistance System for Electric Vehicles

In this work, a predictive eco-driving assistance system (pEDAS) with the goal to assist drivers in improving their driving style and thereby reducing the energy consumption in battery electric vehicles while enhancing the driving safety and comfort is introduced and evaluated. pEDAS in this work is equipped with two model predictive controllers (MPCs), namely reference-tracking MPC and car-following MPC, that use the information from onboard sensors, signal phase and timing (SPaT) messages from traffic light infrastructure, and geographical information of the driving route to compute an energy-optimal driving speed. An optimal speed suggestion and informative advice are indicated to the driver using a visual feedback. pEDAS provides continuous feedback and encourages the drivers to perform energy-efficient car-following while tracking a preceding vehicle, travel at safe speeds at turns and curved roads, drive at energy-optimal speed determined using dynamic programming in freeway scenarios, and travel with a green-wave optimal speed to cross the signalized intersections at a green phase whenever possible. Furthermore, to evaluate the efficacy of the proposed pEDAS, user studies were conducted with 41 participants on a dynamic driving simulator. The objective analysis revealed that the drivers achieved mean energy savings up to 10%, reduced the speed limit violations, and avoided unnecessary stops at signalized intersections by using pEDAS. Finally, the user acceptance of the proposed pEDAS was evaluated using the Technology Acceptance Model (TAM) and Theory of Planned Behavior (TPB). The results showed an overall positive attitude of users and that the perceived usefulness and perceived behavioral control were found to be the significant factors in influencing the behavioral intention to use pEDAS.Comment: Submitted to Transportation Research Part C: Emerging Technologies Journa