| 1. |
- Habibovic, Azra, 1982
(författare)
-
Analyzing real-world data to promote development of active safety systems that reduce car-to-vulnerable road user accidents
- 2012
-
Doktorsavhandling (övrigt vetenskapligt/konstnärligt)abstract
- 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.
|
|
| 2. |
- Habibovic, Azra, 1982, et al.
(författare)
-
Causation mechanisms in car-to-vulnerable road user crashes: Implications for active safety systems
- 2012
-
Ingår i: Accident Analysis and Prevention. - : Elsevier BV. - 0001-4575. ; 49, s. 493-500
-
Tidskriftsartikel (refereegranskat)abstract
- 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.
|
|
| 3. |
- Habibovic, Azra, 1982, et al.
(författare)
-
DREAMi – Final Report
- 2013
-
Rapport (övrigt vetenskapligt/konstnärligt)abstract
- Traditionellt har data från trafikolyckor varit den enda tillgängliga informationskällan som använts för att förstå hur och varför säkerhetskritiska trafiksituationer uppstår. I synnerhet har djupstudier av olyckor varit viktiga eftersom de möjliggör en detaljerad inblick i händelseförloppet. Olycksdatabaser med djupstudier innehåller dock ett begränsat antal fall och ger i princip ingen tidsserieinformation om händelserna som leder till olyckan.För att kompensera för dessa brister har naturalistiska körstudier utvecklats. I sådana studier körs fordon under verkliga trafikförhållanden och är instrumenterade med kameror och andra sensorer för att samla information om förare, fordon och omgivningen. Med denna metod är det oftast möjligt att observera ett stort antal säkerhetskritiska situationer, så kallade incidenter.DREAMi har undersökt om data från naturalistiska körstudier kan användas för att förstå orsaker som leder till incidenter. Data var insamlad i ett annat projekt i Japan och inkluderade bl.a. videoinspelningar av föraren och omgivningen. För att identifiera och koda orsakerna använde projektet Driving Reliability and Error Analysis Method (DREAM). Eftersom DREAM utvecklats för analys av olycksorsaker baserat på information från djupstudier var det nödvändigt att göra modifieringar i metoden för att anpassa den till information tillgänglig i incidenter på video från naturalistiska körstudier. DREAMi har därmed skapat en metod som är unik och finns ännu inte inom trafiksäkerhetsforskning. Den modifierade metoden har med stor framgång applicerats på 90 fotgängarincidenter insamlade i Japan För att undersöka hur den modifierade metoden fungerar i praktiken, har projektet applicerat den på 90 fotgängarincidenter insamlade i Japan.Detta projekt var också ett första forskningssamarbete inom området trafiksäkerhet mellan SAFER - Vehicle and Traffic Safety Center at Chalmers och Japan Automotive Research Institute (JARI). Samarbetet har stärkt trafiksäkerhetsforskningen i båda länderna, samt gjort SAFER mer attraktiv på den internationella arenan. I synnerhet har DREAMi använts för att motivera SAFERs deltagande i internationella projekt som ANNEXT och US SHRP2.
|
|
| 4. |
- Habibovic, Azra, 1982, et al.
(författare)
-
Driver behavior in car-to-pedestrian incidents: An application of the Driving Reliability and Error Analysis Method (DREAM)
- 2013
-
Ingår i: Accident Analysis and Prevention. - : Elsevier BV. - 0001-4575. ; 50, s. 554-565
-
Tidskriftsartikel (refereegranskat)abstract
- To develop relevant road safety countermeasures, it is necessary to first obtain an in-depth understanding of how and why safety-critical situations such as incidents, near-crashes, and crashes occur. Video-recordings from naturalistic driving studies provide detailed information on events and circumstances prior to such situations that is difficult to obtain from traditional crash investigations, at least when it comes to the observable driver behavior. This study analyzed causation in 90 video-recordings of car-to-pedestrian incidents captured by onboard cameras in a naturalistic driving study in Japan. The Driving Reliability and Error Analysis Method (DREAM) was modified and used to identify contributing factors and causation patterns in these incidents. Two main causation patterns were found. In intersections, drivers failed to recognize the presence of the conflict pedestrian due to visual obstructions and/or because their attention was allocated towards something other than the conflict pedestrian. In incidents away from intersections, this pattern reoccurred along with another pattern showing that pedestrians often behaved in unexpected ways. These patterns indicate that an interactive advanced driver assistance system (ADAS) able to redirect the driver's attention could have averted many of the intersection incidents, while autonomous systems may be needed away from intersections. Cooperative ADAS may be needed to address issues raised by visual obstructions.
|
|
| 5. |
- Habibovic, Azra, 1982
(författare)
-
Reduction of Vulnerable Road User accidents in urban intersections: Needs and challenges in designing Advanced Driver Assistance Systems
- 2009
-
Licentiatavhandling (övrigt vetenskapligt/konstnärligt)abstract
- 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.
|
|
| 6. |
- Habibovic, Azra, 1982, et al.
(författare)
-
Requirements of a system to reduce car-to-vulnerable road user crashes in urban intersections
- 2011
-
Ingår i: Accident Analysis and Prevention. - : Elsevier BV. - 0001-4575. ; 43:4, s. 1570-1580
-
Tidskriftsartikel (refereegranskat)abstract
- 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.
|
|
| 7. |
- Klingegard, Maria, et al.
(författare)
-
Drivers' Ability to Engage in a Non-Driving Related Task While in Automated Driving Mode in Real Traffic
- 2020
-
Ingår i: IEEE Access. - 2169-3536 .- 2169-3536. ; 8, s. 221654-221668
-
Tidskriftsartikel (refereegranskat)abstract
- 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' 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.
|
|
| 8. |
- Ljung Aust, Mikael, 1973, et al.
(författare)
-
Manual for DREAM version 3.2
- 2012
-
Rapport (övrigt vetenskapligt/konstnärligt)abstract
- The Driving Reliability and Error Analysis Method (DREAM) is based on the Cognitive Reliability and Error Analysis Method (CREAM; Hollnagel, 1998). CREAM was developed to analyse accidents within process control domains such as nuclear power plants and train operation, and DREAM is an adaptation of CREAM to suit the road traffic domain. The purpose of DREAM is to make it possible to systematically classify and store accident and incident causation information. This means that DREAM, like all other methods for accident/incident analysis, is not a provider but an organiser of explanations. For any of the contributing factor categories available in DREAM to be used, it must be supported by relevant empirical information. DREAM in itself cannot tell us why accidents happen (if it could, we would need neither on-scene investigations nor interviews).DREAM includes three main components: an accident model, a classification scheme and a detailed procedure description which step by step goes through what needs to be done in order to perform a DREAM analysis on an investigated accident/incident. Below, the accident model will be given more detailed descriptions. After this follows a description of the classification scheme, and then comes the analysis process, including example cases and recommendations for how to do the categorisation in certain typical scenarios.
|
|
| 9. |
- Tabone, Wilbert, et al.
(författare)
-
Vulnerable road users and the coming wave of automated vehicles: Expert perspectives
- 2021
-
Ingår i: Transportation Research Interdisciplinary Perspectives. - : Elsevier BV. - 2590-1982. ; 9
-
Tidskriftsartikel (refereegranskat)abstract
- Automated driving research over the past decades has mostly focused on highway environments. Recent technological developments have drawn researchers and manufacturers to look ahead at introducing automated driving in cities. The current position paper examines this challenge from the viewpoint of scientific experts. Sixteen Human Factors researchers were interviewed about their personal perspectives on automated vehicles (AVs) and the interaction with VRUs in the future urban environment. Aspects such as smart infrastructure, external human-machine interfaces (eHMIs), and the potential of augmented reality (AR) were addressed during the interviews. The interviews showed that the researchers believed that fully autonomous vehicles will not be introduced in the coming decades and that intermediate levels of automation, specific AV services, or shared control will be used instead. The researchers foresaw a large role of smart infrastructure and expressed a need for AV-VRU segregation, but were concerned about corresponding costs and maintenance requirements. The majority indicated that eHMIs will enhance future AV-VRU interaction, but they noted that implicit communication will remain dominant and advised against text-based and instructive eHMIs. AR was commended for its potential in assisting VRUs, but given the technological challenges, its use, for the time being, was believed to be limited to scientific experiments. The present expert perspectives may be instrumental to various stakeholders and researchers concerned with the relationship between VRUs and AVs in future urban traffic.
|
|
