Drivers\u27 Ability to Engage in a Non-Driving Related Task While in Automated Driving Mode in Real Traffic

Engaging in non-driving related tasks (NDRTs) while driving can be considered distracting and safety detrimental. However, with the introduction of highly automated driving systems that relieve drivers from driving, more NDRTs will be feasible. In fact, many car manufacturers emphasize that one of the main advantages with automated cars is that it "frees up time" for other activities while on the move. This paper investigates how well drivers are able to engage in an NDRT while in automated driving mode (i.e., SAE Level 4) in real traffic, via a Wizard of Oz platform. The NDRT was designed to be visually and cognitively demanding and require manual interaction. The results show that the drivers\u27 attention to a great extent shifted from the road ahead towards the NDRT. Participants could perform the NDRT equally well as when in an office (e.g. correct answers, time to completion), showing that the performance did not deteriorate when in the automated vehicle. Yet, many participants indicated that they noted and reacted to environmental changes and sudden changes in vehicle motion. Participants were also surprised by their own ability to, with ease, disconnect from driving. The presented study extends previous research by identifying that drivers to a high extent are able to engage in a NDRT while in automated mode in real traffic. This is promising for future of automated cars ability to "free up time" and enable drivers to engage in non-driving related activities

Ihtiofauna slivnog područja reke Krivaje

Terenski dio ihtioloških istraživanja rijeke Krivaje i njenih pritoka obavljen je u periodu oktobar-novembar 2010. godine na području opština Olovo, Vareš i Zavidovići za potrebe izrade Ribarske osnove za područje Zeničko-dobojskog kantona. Izlov ribe je obavljen elektroagregatima marke “Honda”: „FEG 15.000“ snage 15 kW i „OHV 5.5“ snage 3,0 kW. Elektro-ribolov je izvršen na 18.870 m vodotokova, odnosno na 591.140 m2 ukupne vodene površine. Prikupljeni uzorci ribe obrađeni su na terenu i vraćeni u njihovo prirodno stanište, dok je manji broj reprezentativnih primjeraka fiksiran u 4%-tnom formaldehidu i dopremljen u laboratorije Centra za akvakulturu i ribarstvo Poljoprivredno-prehrambenog fakulteta Sarajevo na dalju analizu. Sistematska determinacija riba je vršena po Vukoviću i Ivanoviću (1971) i Sofradžiji (2009). Najveću brojnost u mješovitoj populaciji riba u rijeci Krivaji imale su jedinke iz porodice Cyprinidae sa procentualnim učešćem od 93,92%. Ostale evidentirane vrste iz porodica Thymalidae, Cottidae, Salmonidae i Cobitidae bile su zastupljene od 0,33-2,64%. Najveću brojnost iz pritoka rijeke Krivaje imale su vrste iz porodice Cyprinidae sa procentualnim učešćem od 54,93%. Nižu brojnost u istraživanim pritokama rijeke Krivaje imale su vrste iz porodice Salmonidae 31,54% i Thymallidae sa 9,34%. Najnižu brojnost imale su vrste iz porodice Cottidae sa 4,53%. Na osnovu dobivenih podataka o kvantitativno-kvalitativnom sastavu ihtiofaune sliva rijeke Krivaje, generalno se može zaključiti da ovo istraživano područje ima zadovoljavajuće ekološke uslove za život mnogih vrsta riba

Driver adherence to recommendations from support systems improves if the systems explain why they are given: a simulator study

This paper presents a large-scale simulator study on driver adherence to recommendations given by driver support systems, specifically eco-driving support and navigation support. 123 participants took part in this study, and drove a vehicle simulator through a pre-defined environment for a duration of approximately 10 min. Depending on the experimental condition, participants were either given no eco-driving recommendations, or a system whose provided support was either basic (recommendations were given in the form of an icon displayed in a manner that simulates a heads-up display) or informative (the system additionally displayed a line of text justifying its recommendations). A navigation system that likewise provided either basic or informative support, depending on the condition, was also provided. Effects are measured in terms of estimated simulated fuel savings as well as engine braking/coasting behaviour and gear change efficiency. Results indicate improvements in all variables. In particular, participants who had the support of an eco-driving system spent a significantly higher proportion of the time coasting. Participants also changed gears at lower engine RPM when using an eco-driving support system, and significantly more so when the system provided justifications. Overall, the results support the notion that providing reasons why a support system puts forward a certain recommendation improves adherence to it over mere presentation of the recommendation. Finally, results indicate that participants’ driving style was less eco-friendly if the navigation system provided justifications but the eco-system did not. This may be due to participants considering the two systems as one whole rather than separate entities with individual merits. This has implications for how to design and evaluate a given driver support system since its effectiveness may depend on the performance of other systems in the vehicle

Analyzing real-world data to promote development of active safety systems that reduce car-to-vulnerable road user accidents

The overall objective of the thesis is to explore various types of real-world road traffic data and to assess the extent to which they can inform the design of active safety systems that aim to prevent car-to-vulnerable road user (VRU) accidents. A combined analysis of in-depth and police reported accident data provided information on driver behavior and contextual variables, which is valuable for the development of active safety systems. An analysis of the in-depth data also revealed information about VRU behavior that is relevant for these systems. A key finding from these analyses is that the car drivers commonly did not see the VRUs due to visual obstructions in the traffic environment, misinterpretation of the traffic situation, and/or an inadequate plan of action. The VRUs, on the other hand, saw the cars but they still misunderstood the situation, made an inadequate plan of action, or both. These findings indicate that active safety systems should help drivers to notice the VRUs in time, while the VRUs would benefit from systems helping them to correctly understand the traffic situation. The findings also suggest a need for a variety of cooperative active safety systems, risk assessment algorithms able to predict the intentions of road users to cross the road, and human-machine interfaces capable of directing road users’ attention towards the most critical event. Similar findings were obtained when driver behavior and contextual variables were investigated using video-recordings of car-to-pedestrian incidents. However, these data enabled more detailed analysis of driver attention allocation as well as driver interaction with the vehicle, other road users, and the traffic environment. Finally, an analysis of data on pedestrian behavior and car dynamics from normal interactions in traffic showed that a statistical model, based on car speed and its distance to the point of potential collision and on pedestrian distance to the road, speed and head orientation, could be used to determine the likelihood of a pedestrian entering the road. This can then be combined with commonly used deterministic approaches to estimate when a warning or other action by an active safety system should be initiated. To conclude, each of the four data sources explored here has its own advantages and disadvantages; information combined from analysis of these sources provides an improved understanding of the traffic situations involving VRUs, which is crucial in the development of future active safety systems

Teknikstöd för hastighetsefterlevnad hos yrkestrafiken

Rapporten är framtagen med ekonomiska bidrag från Trafikverket, Skyltfonden. Ståndpunkter och slutsatser i rapporten reflekterar författaren och överensstämmer inte med nödvändighet med Trafikverkets ståndpunkter och slutsatser inom rapportens ämnesområden.Rätt hastighet och ett bra körsätt är positivt för miljön och ett effektivt sätt att minska bränslekostnader och bullerstörningar samtidigt som det bidrar till bättre trafiksäkerhet. Syftet med studien är att utveckla kunskap om tekniska system som bidrar till bättre efterlevnad av hastighetsbegränsningar hos yrkestrafiken. Målet är att ta reda på huruvida dessa system kan användas av transportföretag som t.ex. lastbilsåkerier för egenkontroll av hastighet. Studien fokuserar på system som stödjer föraren i realtid och/eller registrerar data för uppföljning, dvs. inte hastighetskameror, radar och dylikt som används vid hastighetskontroller idag. Studien är baserad på litteraturanalys, enkätstudie och intervjuer med relevanta aktörer i Sverige inklusive transportföretag, systemtillverkare, fordonstillverkare och transportköpare. Resultaten tyder på att flera transportföretag har teknisk utrustning i sina fordon som på ett eller annat sätt adresserar hastighetsefterlevnad. Slutsatsen är att egenkontroll av hastighetsefterlevnaden i yrkestrafikens fordonsflottor kan vara möjlig med tekniska system som finns i vissa fordon idag. Om myndigheter och/eller transportköparna ska ställa krav på egenkontroll av hastigheten måste dock kraven vara i linje med systemens tekniska begräsningar. En annan begräsning som behöver tas i beaktandet är att de flesta registrerande system är beroende av digitala kartor som är baserade på hastighetsbegränsningar i Nationella vägdatabasen (NVDB) som i sig kan innehålla bristfällig och ej heltäckande information. (Trafikverkets beteckning: TRV 2019-847 ”Tekniska system för bättre efterlevnad av hastighetsbegränsningar för yrkestrafik”)Projektet har utförts och slutrapporterats under 201

Reduction of Vulnerable Road User accidents in urban intersections: Needs and challenges in designing Advanced Driver Assistance Systems

Advanced driver assistance systems (ADAS) can be used to prevent accidents, or to reduce their severity. It is essential to determine what functional requirements such systems should fulfill to meet drivers’ support needs. An understanding of the underlying contributing factors and context in which the accidents occur is therefore crucial. One aim of this thesis is to identify drivers’ support needs in accidents involving vulnerable road users (VRUs) in urban intersections. Another aim is to identify the most promising ADAS for this accident type and to derive functional sensor, collision detection, and human-machine interface (HMI) requirements. A third aim is to develop an ADAS concept based on these requirements. Microscopic and macroscopic accident data were analyzed. The microscopic data, obtained from the European project SafetyNet, consisted of causation charts describing contributing factors for 60 accidents. These charts have been compiled by means of the SafetyNet Accident Analysis System (SNACS). This thesis aggregated the individual causation charts for the drivers. The macroscopic data, obtained from the Swedish national accident database STRADA, consisted of 9702 accidents. The results revealed that the most frequent contributing factor was failure to observe the VRUs. This was mostly due to reduced visibility, reduced awareness, and/or insufficient comprehension. An ADAS should therefore help the drivers to notice the VRUs and enhance their ability to interpret the development of events in the near future. Such a system should include a combination of imminent and cautionary collision warnings, with additional support in the form of information about intersection geometry and traffic regulations. The warnings should preferably be deployed via in-vehicle HMI and according to the likelihood of accident risk. To enable timely warnings, it may be necessary to predict road user intentions approximately 4 seconds ahead. The study also showed that the system must be able to operate under a variety of road, weather, and light conditions. It should have the capacity to support drivers when their view is obstructed by physical objects. To this end, it is recommended that onboard sensors be complemented by cooperative infrastructure-car systems. Consequently, an ADAS concept utilizing a vision based VRU detection system in the infrastructure is proposed. The VRU position and velocity were continuously broadcast to the cars in the vicinity. The cars used global positioning systems to determine their own position and velocity. Based on these data, each car’s system estimated its own collision risk with each VRU. If this risk was high, information about the intersection and a cautionary warning were issued to the driver via an in-vehicle HMI. An initial evaluation of this conceptual system indicated that several technical factors and human aspects need further investigation and development. These include mainly detection and tracking of road users as well as prediction of their intentions

Analyzing real-world data to promote development of active safety systems that reduce car-to-vulnerable road user accidents

The overall objective of the thesis is to explore various types of real-world road traffic data and to assess the extent to which they can inform the design of active safety systems that aim to prevent car-to-vulnerable road user (VRU) accidents. A combined analysis of in-depth and police reported accident data provided information on driver behavior and contextual variables, which is valuable for the development of active safety systems. An analysis of the in-depth data also revealed information about VRU behavior that is relevant for these systems. A key finding from these analyses is that the car drivers commonly did not see the VRUs due to visual obstructions in the traffic environment, misinterpretation of the traffic situation, and/or an inadequate plan of action. The VRUs, on the other hand, saw the cars but they still misunderstood the situation, made an inadequate plan of action, or both. These findings indicate that active safety systems should help drivers to notice the VRUs in time, while the VRUs would benefit from systems helping them to correctly understand the traffic situation. The findings also suggest a need for a variety of cooperative active safety systems, risk assessment algorithms able to predict the intentions of road users to cross the road, and human-machine interfaces capable of directing road users’ attention towards the most critical event. Similar findings were obtained when driver behavior and contextual variables were investigated using video-recordings of car-to-pedestrian incidents. However, these data enabled more detailed analysis of driver attention allocation as well as driver interaction with the vehicle, other road users, and the traffic environment. Finally, an analysis of data on pedestrian behavior and car dynamics from normal interactions in traffic showed that a statistical model, based on car speed and its distance to the point of potential collision and on pedestrian distance to the road, speed and head orientation, could be used to determine the likelihood of a pedestrian entering the road. This can then be combined with commonly used deterministic approaches to estimate when a warning or other action by an active safety system should be initiated. To conclude, each of the four data sources explored here has its own advantages and disadvantages; information combined from analysis of these sources provides an improved understanding of the traffic situations involving VRUs, which is crucial in the development of future active safety systems

Requirements of a system to reduce car-to-vulnerable road user crashes in urban intersections

Intersection crashes between cars and vulnerable road users (VRUs), such as pedestrians and bicyclists, often result in injuries and fatalities. Advanced driver assistance systems (ADASs) can prevent, or mitigate, these crashes. To derive functional requirements for such systems, an understanding of the underlying contributing factors and the context in which the crashes occur is essential. The aim of this study is to use microscopic and macroscopic crash data to explore the potential of information and warning providing ADASs, and then to derive functional sensor, collision detection, and human-machine interface (HMI) requirements. The microscopic data were obtained from the European project SafetyNet. Causation charts describing contributing factors for 60 car-to-VRU crashes had been compiled and were then also aggregated using the SafetyNet Accident Causation System (SNACS). The macroscopic data were obtained from the Swedish national crash database, STRADA. A total of 9702 crashes were analyzed. The results show that the most frequent contributing factor to the crashes was the drivers' failure to observe VRUs due to reduced visibility, reduced awareness, and/or insufficient comprehension. An ADAS should therefore help drivers to observe the VRUs in time and to enhance their ability to interpret the development of events in the near future. The system should include a combination of imminent and cautionary collision warnings, with additional support in the form of information about intersection geometry and traffic regulations. The warnings should be deployed via an in-vehicle HMI and according to the likelihood of crash risk. The system should be able to operate under a variety of weather and light conditions. It should have the capacity to support drivers when their view is obstructed by physical objects. To address problems that vehicle-based sensors may face in this regard, the use of cooperative systems is recommended. (C) 2011 Elsevier Ltd. All rights reserved

Causation mechanisms in car-to-vulnerable road user crashes: Implications for active safety systems

Vulnerable road users (VRUs), such as pedestrians and bicyclists, are often involved in crashes with passenger cars. One way to prevent these crashes is to deploy active safety systems that support the car drivers and/or VRUs. However, to develop such systems, a thorough understanding of crash causation mechanisms is required. The aim of this study is to identify crash causation mechanisms from the perspective of the VRUs, and to explore the implications of these mechanisms for the development of active safety systems. Data originate from the European project SafetyNet, where 995 crashes were in-depth investigated using the SafetyNet Accident Causation System (SNACS). To limit the scope, this study analyzed only intersection crashes involving VRUs. A total of 56 VRU crashes were aggregated. Results suggest that, while 30% of the VRUs did not see the conflict car due to visual obstructions in the traffic environment, 70% of the VRUs saw the car before the collision, but still misunderstood the traffic situation and/or made an inadequate plan of action. An important implication that follows from this is that, while detection of cars is clearly an issue that needs to be addressed, it is even more important to help the VRUs to correctly understand traffic situation (e.g., does the driver intend to slow down, and if s/he does, is it to let the VRU cross or for some other reason?). The former issue suggests a role for various cooperative active safety systems, as the obstacles are generally impenetrable with regular sensors. The latter issue is less straightforward. While various systems can be proposed, such as providing gap size estimation and reducing the car speed variability, the functional merits of each such a system need to be further investigated

DREAMi – Final Report

Traditionellt har data fr\ue5n trafikolyckor varit den enda tillg\ue4ngliga informationsk\ue4llan som anv\ue4nts f\uf6r att f\uf6rst\ue5 hur och varf\uf6r s\ue4kerhetskritiska trafiksituationer uppst\ue5r. I synnerhet har djupstudier av olyckor varit viktiga eftersom de m\uf6jligg\uf6r en detaljerad inblick i h\ue4ndelsef\uf6rloppet. Olycksdatabaser med djupstudier inneh\ue5ller dock ett begr\ue4nsat antal fall och ger i princip ingen tidsserieinformation om h\ue4ndelserna som leder till olyckan.F\uf6r att kompensera f\uf6r dessa brister har naturalistiska k\uf6rstudier utvecklats. I s\ue5dana studier k\uf6rs fordon under verkliga trafikf\uf6rh\ue5llanden och \ue4r instrumenterade med kameror och andra sensorer f\uf6r att samla information om f\uf6rare, fordon och omgivningen. Med denna metod \ue4r det oftast m\uf6jligt att observera ett stort antal s\ue4kerhetskritiska situationer, s\ue5 kallade incidenter.DREAMi har unders\uf6kt om data fr\ue5n naturalistiska k\uf6rstudier kan anv\ue4ndas f\uf6r att f\uf6rst\ue5 orsaker som leder till incidenter. Data var insamlad i ett annat projekt i Japan och inkluderade bl.a. videoinspelningar av f\uf6raren och omgivningen. F\uf6r att identifiera och koda orsakerna anv\ue4nde projektet Driving Reliability and Error Analysis Method (DREAM). Eftersom DREAM utvecklats f\uf6r analys av olycksorsaker baserat p\ue5 information fr\ue5n djupstudier var det n\uf6dv\ue4ndigt att g\uf6ra modifieringar i metoden f\uf6r att anpassa den till information tillg\ue4nglig i incidenter p\ue5 video fr\ue5n naturalistiska k\uf6rstudier. DREAMi har d\ue4rmed skapat en metod som \ue4r unik och finns \ue4nnu inte inom trafiks\ue4kerhetsforskning. Den modifierade metoden har med stor framg\ue5ng applicerats p\ue5 90 fotg\ue4ngarincidenter insamlade i Japan F\uf6r att unders\uf6ka hur den modifierade metoden fungerar i praktiken, har projektet applicerat den p\ue5 90 fotg\ue4ngarincidenter insamlade i Japan.Detta projekt var ocks\ue5 ett f\uf6rsta forskningssamarbete inom omr\ue5det trafiks\ue4kerhet mellan SAFER - Vehicle and Traffic Safety Center at Chalmers och Japan Automotive Research Institute (JARI). Samarbetet har st\ue4rkt trafiks\ue4kerhetsforskningen i b\ue5da l\ue4nderna, samt gjort SAFER mer attraktiv p\ue5 den internationella arenan. I synnerhet har DREAMi anv\ue4nts f\uf6r att motivera SAFERs deltagande i internationella projekt som ANNEXT och US SHRP2