Large Scale Naturalistic Driving Observations in Europe. Nicole van Nes (SWOV) Project Coordinator

Transcription

1 Large Scale Naturalistic Driving Observations in Europe Nicole van Nes (SWOV) Project Coordinator

2 Content of the presentation About SWOV What is Naturalistic Driving? Naturalistic Driving studies around the globe UDRIVE Study design Observation equipment Research questions

3 SWOV About SWOV Dutch National Road Safety Research Institute About 30 FTE researchers

4 Every day 275 crashes 52 injury crashes 1-2 fatalities

5 Prevent crashes Reduce injury Save lives

6 A safe traffic system is of great societal value

7 System approach

8 System approach Prevent crash Limit crash impact Macro factors Traffic system High risk situations High risk groups High risk behaviour Near crash Crash Limit injury Injury treatmen t

9 Influential trends ageing society traffic automation urbanisation vehicle automation

10 The system approach and ITS There is a strong believe that automation of the traffic system will contribute to road safety Prevent crash Limit crash impact Macro factors Traffic system High risk situations High risk groups High risk behaviour Near crash Crash Limit injury Injury treatment Technological developments, Car sharing schemes Navigation support systems, traffic flow optimisation, automation of pedestrians and cyclists Traffic jam information, alternative route information, weather and road condition, road constructions, Support systems for elderly drivers, young drivers, Visually or auditory impaired drivers ISA, (AC)CC, Fatigue detection, Distraction detection, alcohol-lock, Workload management Active safety systems, Collision warning systems Passive safety systems E-call Step by step new systems could eliminate risks to prevent crashes and reduce injury 10

11 Emerging research topics In the transition towards higher levels of automation, safety is critical! Prevent crash Limit crash impact Macro factors Traffic system High risk situations High risk groups High risk behaviour Near crash Crash Limit injury Injury treatment System failures Privacy protection Cyber security User acceptance (safety) effects at different penetration levels of automated/ cooperative systems, Legislation limitations e.g. applicability in certain conditions (e.g. rain, dark) Interaction of motorized vehicles with cyclists and pedestrians Elderlly Workload management Situational Awareness Transition of control Adverse behavioural adaptation Overreliance Liability 11

12 Automation of the traffic system Transition of control Workload and situational awareness Vulnerable road users Elderly (Safety) effects at different levels of penetration safety issues during the transition complementary methods interplay between technology and behaviour learn from other areas Naturalistic Driving On-road test drives Test drives with (partly) automated vehicles Simulator studies Micro-simulations

13 What is Naturalistic Driving?

14 Naturalistic Driving Observation New and promising research method

15 Use of in-vehicle systems

16 Distraction and inattention Mobile phone use

17 Interactions with Vulnarable Road Users

18 And also.

19 Data coding software

20 Data coding software

21 Value of Naturalistic Driving data Natural behaviour in natural surrounding No experimental interventions Insight look over the shoulder of the driver Allows to study exposure, prevalence and risk Direct observation of conflicts and (near) crashes Exact and detailed information what preceded No bias by post-hoc reporting Laboratory study Driving simulator study Instrumented car study

22 Naturalistic Driving Studies Study characteristics Overview of previous and on-going studies

23 Data collection technology Continuous advanced measures incl. continuous video 100 car study INTERACTION UDRIVE SHRP2 Continuous basic measures + event based video Prologue Basic measures Continuous or event based DaCoTA ND monitoring Small <100 vehicle years Medium >100 vehicle years Large >1.000 veh. years Extra large > veh. years Sample size

24 450 DAS 150 DAS 300 DAS

25 UDRIVE versus SHRP2 SHRP2 UDRIVE Australian NDS Budget ~70 M$ ~13 M$ (10M ) ~3,4 M$ Main objective Building a database Building a database + performing analyses Vehicle types Cars Cars, PTWs and trucks Cars Driving hours Camera views Building a database Participants 3100, 12 months 210, 21 months 400, 6 months

26 UDRIVE Study design Observation equipment Research questions

27 UDRIVE Large scale European Naturalistic Driving study Duration of the observation is 21 months per vehicle (1 wave) PTW s 40 in total 15 vehicles in Austria 25 vehicles in Spain Person cars 120 in total France, Germany, Poland, UK 30 vehicles per country Trucks 50 in total Netherlands

28 Project timeline SP1 Design oct nov dec jan feb mar apr may jun jul aug sep oct nov dec jan feb mar apr may jun jul aug sep oct nov dec jan feb mar apr may jun jul aug sep oct nov dec jan feb mar apr may jun jul aug sep Study design and research questions SP2 Data Management DAS specification and developemnt pilot DAS available for testing installation Data management operations SP3 Data collection pilot testing Data collection SP4 Data analyses Data analyses SP5 Impact Impact

29 Participant stratification Cars: Multi-driver selection to enlarge sample size Cars year year male 7 (28) 8 (32) female 7 (28) 8 (32) PTWs PTWs year year male female 9 10 Trucks: availability of driver and vehicles depends on participating fleets

30 Vehicle types 3 cars types: Renault Clio 3 (small car) Renault Clio 4 (small car) Renault Me gane 3 (Medium-sized family car) 1 PTW type: Piaggio Liberty 2 Truck types: Volvo, medium sized for city deliveries

31 Progress on participant recruitment Cars 27 June 2014 England 6 France 1 Germany 18 Poland 3 TOTAL 28 PTW 27 June 2014 Austria 4 Spain 2 TOTAL 6 Trucks 27 June 2014 Netherlands 36 TOTAL 36

32 The Data Acquisition System (DAS) Time Time-To-Collision (TTC) Time of day Time-Headway (THW) Weather Longitudinal Acceleration Road section Lateral Acceleration Road condition Yaw rate Number of lanes Steering wheel angle Lane position BendDectection LaneWidth Gear selection Type of road Turn indicator signal identification of urban area Accelerator pedal position Road inclination Brake pedal position Road curvature Clutch SV time of entering enchroachment zone Speed Limit POV time of entering enchroachment zone Visibility conditions Start of evasive maneuver Occlusion of sight (inside) Type of evasive maneuver right turn maneuver GPS coordinates left turn maneuver heading Headlight activity Link id / digital map data Fuel level Traffic light status Optical size of POV (principal Presence of road works other vehicle) Traffic density POV exentricity angle Intersection type POV type Intersection type Brake light onset of POV Traffic control POV motion prior to entering encroachment zone Trip summary Time of POV stopped Driver demographics Narrative Occlusion of objects (outside) Personalty characteristics Distance to vehicle ahead Personalty characteristics Speed of vehicle ahead Driver id Presence and position of other vehicles Driver state Traffic direction Driver activity Ambient light Gaze coding Windshield wipers Long eye closure coding Pedestrian/Cyclist detection Gaze eccentricity Type of vehicle DriverReaction Seatbelt use distance to cyclists Helmet use Distance to pedestrian/cyclist Camera A: view on front Time-to-collision from pedestrian/cyclist Camera B: towards right side the vehicle Pedestrian/Cyclist head direction Camera C: towards left side the vehicle Pedestrian/Cyclist density Camera C: view on driver's face Pedestrian/Cyclist age Camera D: view on secondary tasks pedestrian/cyclist gender Camera E: showing passengers Pedestrian/Cyclist activity Camera F: showing feet at pedals Pedestrian/Cyclist conspiquity Camera G: view behind vehicle Traffic and pedestrian signals Camara position for PTWs Detection of traffic calming measures in the urban area SCE type Engine RPM/engine torque Event severity current intant. Consumption Number of passengers in vehicle engine temperature Sound recorded from cabin outside temperature In-vehicle button presses estimation of vehicle mass Driving task difficulty Heading of the vehicle relative to lane axis Vehicle Speed Type of lane marking Relative velocity PTW rider interaction with helmet radio or phone device Relative acceleration Vehicle direction

33 MobilEye smart machine vision

34 Cameras Position Cars Forward cameras Feet camera Face camera Driver s action camera Passenger compartment camera Right blind spot camera* *Integration to be validated

35 Cameras Position Cars Forward cameras Feet camera Face camera Driver s action camera Passenger compartment camera Right blind spot camera*

36 Cameras Position Cars Front view wide (3 cams) Face view (1 cam) Driver s activity (1 cam) a bit more to the left Driver s feet (1 cam) Cabin view (1 cam) Right blind spot* (1 cam) 8 cameras

37 Cameras Position Trucks Forward cameras Feet camera Face camera (just 1) Driver s action camera Passenger compartment camera Blind spot camera* *Integration to be validated

38 *Integration to be validated Cameras Position Trucks Forward cameras 78 Feet camera Face camera (just 1) Driver s action camera Passenger compartment camera Blind spot camera*

39 *Integration to be validated Cameras Position Trucks 8 cameras Front view (3 cams) color Face view (1 cam) a bit farther away than shown here Blind spot views* (2 cams) grey scale Driver s and cabin activity (1 cam) grey scale Driver s feet (1 cam) grey scale ** In lack of side view examples from trucks, the ones from cars instead to approximate visualization are used *** The forward view will be a 2 camera wide view and a 1 camera center view (see previous slide)

40 Cameras Position PTW Top case Forward cameras Face camera Side cameras Rear camera

41 Cameras Position PTW 78 Forward cameras 78 Face camera Side cameras Rear camera Top case

42 Cameras Position PTW PTWs Front view (1 cam) 16:9 wide screen ratio if possible Head view (1 cam) grey scale Side Views (2 cams) wide field of view Rear view (1 cam) grey scale cameras * In lack of view examples from PTWs, the rear view from cars instead to approximate visualization is used ** The side views will be indeed with a much wider field of view than seen here (see previous slide)

43 Main research areas Crash causation and risk Everyday driving Distraction and inattention Pedestrians and cyclists Motorcycle behaviour Eco-driving

44 Crash causation and risks Crash causation ( holistic ) How can contributing-factor chain schemas be applied to naturalistic road user data? What are the factors that contribute to the occurrence of safety critical events for lead- vehicle and intersection conflict scenarios for cars and trucks? Are there driver/vehicle/environment factors that frequently occur together in a safety critical events? Risk calculation What are the risks of different driver behaviours? Is there a difference in the driving related risks when stratified by road type, vehicle type and country? What is the risk of disregarding safety precautions?

45 Everyday driving Descriptive analysis of everyday driving To what extent are driver factors associated with risky behavior? To what extent are environmental factors associated with risky behavior? To what extent are driver assistance systems used? To what extent are seatbelts used? How does traffic culture influence driving behavior?

46 Distraction and inattention Attention selection mechanisms Which perceptual cues reliably capture attention and trigger avoidance maneuvers in SCEs? Why do the reactive attention capture mechanisms, identified in RQ1, sometimes fail and lead to crashes? What factors determine how drivers proactively allocate their attention in anticipation of how a driving situation will unfold and why do these proactive selection mechanisms sometimes fail? Involvement in secondary tasks What are the key factors influencing the willingness of drivers to deliberately engage in secondary tasks such as phone conversation, dialing or texting? How do drivers adapt ongoing secondary task activities to the evolving driving situation? To what extent can an individual s willingness to engage in secondary tasks, and its effects on risk and driving performance, be predicted from psychological tests?

47 Pedestrians and cyclists Drivers interacting with cyclists and pedestrians What characterizes Safety Critical events (SCE) involving motorized traffic and cyclists/pedestrians at intersections? How do car drivers behave at intersections in urban areas where they might encounter cyclists/pedestrians (in normal conditions, not SCE). Which external factors (e.g., intersection design) modify those behaviours? Are the VRU related SCE s identified by the Mobile Eye system (warnings) correct, relevant, reliable and properly timed?

48 Motorcycle behaviour PTWs behavior (based on riding data) What characterizes Safety Critical events (SCE) involving PTWs? Which circumstances (rider, infrastructure, trip) have impact to SCE occurrence? What is riders speed choice in relation to the speed limit and the situation? What characterizes looking behavior of PTW riders in left turn maneuvers? Behavior towards PTWs (based on driving data) What is the role of timely perception of a rider by drivers? (conspicuity)

49 Eco-driving Effects of driving styles on eco driving Does the vehicle power-to-mass ratio affect the driving style? How much do drivers deviate from the speed limit in free flow situations, and why? Is eco-driving and safe driving correlated, through increased anticipation of road infrastructure and traffic situations? Potential effect of eco-driving When do drivers brake and is it necessary to brake in each instance? Is eco-driving a visible characteristic of certain drivers? Do drivers shift gear to avoid high engine speeds and high fuel consumption?

50 Other interesting research topics Outside the UDRIVE project Infrastructure Drowsiness Traffic management Use of ADAS systems

51 Analyses Pre-processing and data enrichment Preliminary Analyses Plan Safety Critical Events

52 Pre-processing and data enrichment Map matching Enrich GPS data with e.g. Speed limits Road type Intersection type Video annotation Central annotations By students Driver ID Classification of SCE candidates into safety relevant categories Base annotations of SCEs Location based annotation of required variables (eg intersection type) development of codebook Local annotations Analyses partners Detailed coding, e.g. eye movements, details about the situation.

53 Preliminary Analyses Plan Main issues: Strategies for triggers Development of algorithms for data selection Definition of Safety Critical Event International discussion New definitions to be developed for VRUs Strategies for annotation Development of annotation code book Use SHRP2 annotation schemas as a basis, but adapt some and add several other variables

54 Data Sharing and Data Protection Concept Data available for post project research Data access for nonpartners

55 Remote access to UDRIVE data Analysis performed remotely on the common dataset Central Data Center (CDC) located at SAFER Hosts all data LD C CDC Local Data Centers (LDC) located at DLR, Volvo and CEESAR Hosts part of data Partner Data Center (PDC) Partner can download data and become PDC LD C LD C

56 Post-project access to UDRIVE data Infrastructure for remote access and data protection Third parties can remotely access all data from CDC, except Personal Identity Data (PID) PID (video, GPS) is only accessible at the premises of CDC or PDC Third parties must apply to analyze data and adhere to the Data Protection Concept Access to data, excluding PID Third Party Access to PID data Remote CDC PDC PDC PDC All data remains in original storage. Data for publications can be extracted based on consent from drivers Third Party At CDC At PDC CDC PDC PDC PDC

57 Sharing UDRIVE data Objectives Access to the data is agreed on in project documents Remote access and Data Protection Concept are tested in the project Obstacles Funding for post-project data storage and access not yet solved Data protection of PID (necessary!)

58 Other European initiatives Application on PTW and cyclist for H2020 FOT-NET DATA Naturalistic Cycling 58

59 3-year EU support action project January 2014 December 2016 Budget 1.8M main objectives: Support efficient sharing and re-use of FOT datasets Develop and promote a framework for sharing data Build a detailed catalogue of available data and tools Create international networking platform

60 Additional Measures e.g. radar, smart camera Continuous advanced measures (incl. video) Data collection technology Road safety Product development + HMI design Road safety Interaction with VRU s Use of ADAS Distraction, inattention and fatigue Sharing of data between projects Continuous basic measures + event based video Crash causation Road safety Basic measures Continuous or event based Small <100 vehicle years Eco-drivng Sustainable society Medium >100 vehicle years Traffic management Traffic management Large >1.000 veh. years Monitoring SPI s Extra large > veh. years Insurance companies + Road safety Sample size

61 Use Naturalistic Driving data to support safe transition towards higher levels of automation Transition of control Workload and situational awareness Vulnerable road users Elderly safety issues during the transition (Safety) effects at different levels of penetration

62 Bicycle instrumentation 62

63 Towards Naturalistic Cycling

64 More information?

65 UDRIVE: some facts and figures Full name: european naturalistic Driving and Riding for Infrastructure & Vehicle safety and Environment Programme: 7 th EU Framework Programme Project type: Collaborative project, large-scale integrating Coordinator: SWOV Institute for Road Safety Research (NL) Consortium: 19 partners, 10 countries Duration: October September 2016 Budget: 10.5 million EU funding: 8 million

66 The consortium: research, industry, interest groups Country Partners Austria KFV Czech Republic CDV Germany BASt, DLR, TU Chemnitz France CEESAR, IFSTTAR, LAB Israel Or Yarok Netherlands SWOV (Coordinator), TNO Poland IBDiM Spain CIDAUT Sweden SAFER, VOLVO UK Universities of Leeds and Loughborough International ERTICO, FIA

67 Advisory Board Kenn Campbell SHRP2 USA Tom Dingus VTTI - USA Carol Flannagan UMTRI USA Mike Regan TARS - Australia David Shinar Ben Gurion University Israel George Yannis NTUA - Greece Veneta Vassileva ACEM - Brussels Carlo van de Weijer TomTom - Netherlands Fred Wegman Delft University of Technology - Netherlands