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  • UDRIVE data is available for further use!

    The UDRIVE Data User Group is a joint initiative of partners to maintain the remote access to the central data center. The partners are open for collaborations. If you are interested to work with them, please find the contact information below:

    DLR Mandy Dotzauer Mandy.Dotzauer@dlr.de
    IFSTTAR Helene Tattegrain helene.tattegrain@ifsttar.fr
    University of Leeds Oliver Carsten O.M.J.Carsten@its.leeds.ac.uk
    SAFER Helena Gellerman helena.gellerman@chalmers.se
    SWOV Nicole van Nes Nicole.van.Nes@swov.nl
    Loughborough University Ruth Welsh R.H.Welsh@lboro.ac.uk

     

    Some partners will host the data at their own premises and are open for collaboration. If you are interested to work with them, please find the contact information below.

    Ceesar  (car data only)   Clement Val clement.val@ceesar.fr
    SWOV (all data) Nicole van Nes Nicole.van.Nes@swov.nl
  • We found differences between EU countries and differences between Europe and US

    great to have cross European ND dataset!

  • Database

    Cross European ND data, 6 countries, 3 vehicle types, descriptives: total hours + stratification by country, vehicle type, gender, and road type

  • Data Acquisition System (DAS)

    UDRIVE provides a unique opportunity to study safety and eco-driving behavior in relationship with personal driving style, infrastructure, congestion and vehicle technology. Unique data derived from a large scale cross European ND study with detailed information from 5-7 camera’s + MobileEye (ME)

    8 cameras:

    Forward cameras

    Feet camera

    Face camera

    Driver’s action camera

    Passenger compartment camera

    Right blind spot camera

     

     

     

     

  • The findings in terms of time headway are rather alarming: Although it is strongly recommended to increase time headway with speed (even measured in time, not in meters) people obviously do the opposite as confirmed by both methods UDRIVE data and Austrian site-based speed data. This is more or less consistent through all the countries and all vehicle categories and it is valid up to a speed of 80 km/h per hour. The most effective intervention: Distance warning and adaptive cruise control. Public campaigning could raise awareness on the issue.

    This is more or less consistent through all the countries and all vehicle categories and it is valid up to a speed of 80 km/h per hour. National differences are very small. The most effective intervention: Assistance systems like distance warning and adaptive cruise control should support optimised time headway with respect to speed. Campaigns should on no account focus on time headway in general, but on its adoption to speed. And further research is needed, how exactly drivers choose time headway.

    In any case, PTW riders in general do not have to worry about being followed too closely by cars, which also means that further research must investigate other reasons than short time headway for rear-end collisions.

  • Safety by numbers applies also for pedestrians. Expectancy is crucial – when pedestrians are present, drivers are better prepared for pedestrians in general and more ready for responding performing unexpected actions (e.g. stepping out )

    The notion of “surprise” plays an important role: when PCWs were preceded by other events involving pedestrians (PCW or DZ), drivers significantly reduced their speeds and were better prepared for a potential conflict with pedestrians.

  • In our analyses car drivers checked their blind spot at only 7% of the right turns at intersections (UK: left turn), and at only 5% of the cases when leaving a roundabout. In the Netherlands the blindspot was checked significantly more than in other countries. The blind spot check frequency of Dutch truck drivers is in the same order of magnitude as Dutch car drivers.
    Failure to perform appropriate visual checks at intersections may have contributed to the 2069 cyclist fatalities in the European Union in 2015. We investigated whether and when drivers perform visual checks for potentially encroaching cyclists during right-turn manoeuvres. Data was collected from 69 car drivers in France, the Netherlands, Poland, and UK, and from 12 Dutch truck drivers. The car dataset consisted of 961 manoeuvres at intersections (trucks: 159) and 852 manoeuvres at roundabouts (trucks: 209). In the six seconds before the manoeuvre, car drivers checked their blind spot in 4% of the intersections and roundabouts. Including the manoeuvre, this frequency increased to 7% at intersections and 5% at roundabouts. The prevalence of cyclist facilities was highest in the Netherlands, which is also the country where blind spot checks were performed most often (27% at intersections, 19% at roundabouts). Dutch truck drivers checked their blind spot at 19% of the intersections and 27% of the roundabouts. When not checking the blind spot, car and truck drivers mostly looked toward the road they were turning into. Our findings may inform driver training to increase awareness of cyclists at urban intersections and roundabouts.

    Manoeuvre Anatomy

     

    Supporting Documents

    D44.1 Interaction with Vulnerable Road Users

    This deliverable analyses the interactions of pedestrians, cyclists and PTWs with passenger cars and trucks. The aim was to identify and understand the everyday behavioural patterns in these interactions as well as the circumstances of safety critical events (SCE). For cyclists, identified SCEs were caused by a combination of infrastructure (a curve or a too narrow road), manoeuvre (often overtaking), the presence of other traffic, and an error or unexpected behaviour of the cyclist (slowing down). For pedestrians, in around three quarters of SCEs, the driver him/herself had spotted the pedestrian in time. In the remaining situations, a warning system could have been of help. For PTWs, the data did not show that car drivers tend to follow them closer than cars or trucks.

  • Vehicle dynamics with powered two-wheelers (PTWs) are much more difficult than for cars: there’s one degree of freeedom more, and, according to the findings of UDRIVE, riders’ preferences in terms of dynamics strongly differ. That means that design of assistance systems for PTWs is more difficult than for cars and the individual skills and preferences of the rider have to be considered.

    It was found that despite the use of a high-quality data acquisition system, detection of high-risk events with powered two-wheelers remains like searching a needle in a stack of hay. Everyday riding behaviour does not detectably differ from emergency behaviour. Most likely this is caused by poor skills of riders and a lack of willingness to perform highly dynamic manoeuvres – being aware of the particular danger of falling off the vehicle. Dynamic preferences of riders strongly differ within the population and even within subjects. And this tolerance for high dynamics seems to influence emergency behaviour as well as everyday behaviour.

    This could be changed by training, but the everyday behaviour would most likely be changed at a time. For naturalistic studies, this phenomenon suggests the use of an incident button, trip records or periodic interviews. For the design of rider assistance systems, this automatically means that systems will have to be highly adaptive with respect to the preferences of a rider.

  • Overall involvement in secondary tasks for trucks is much higher than for cars, about 20% of the total time

    · Most time is spent in food and phone (5% each)
    · Most frequent is operations of vehicle control: 8 (7.78) times/h
    · Out of 5% of phone use, half (2.4 %) was texting, reading or other visual interactions (talking on the phone was relatively small proportion)

  • Car drivers were involved in distracting activities for 10% of the time spend driving · The most common distracting activity is hand-held mobile phone use; this is in total 4.2% of driving time · In Poland, mobile phone use was significantly higher than in other countries (UK, France, Germany)
    This is significantly less than in the U.S. (as reported by Dingus et al) and methodological differences between the U.S. and European studies cannot account for that difference
    There are indications that car drivers attempt to self-regulate their activity: there is considerably more activity when stopped or moving slowly

     

    Supporting documents 

    D41.1 Synthesis of analysis results

    This deliverable presents key results of the analysis performed in UDRIVE Sub-project 4: Data analysis. It also describes the UDRIVE dataset. The analysis in UDRIVE was facilitated by tools such as the quality assurance procedures and data tracking, the SALSA data processing tool, the UDRIVE annotation codebook and high-quality manual annotation of video. The analysis itself is described in short in this report, while details are presented in separate UDRIVE deliverables. In summary, a large variety of analyses was performed on the UDRIVE naturalistic driving data (NDD). Although the efforts and results have been significant and already impact safety measure design and development, the UDRIVE project has only scratched the surface of the analysis potential.

     

  • Auto annotation – if properly trained, automated identification of distracting activities is feasible
  • Anticipating traffic when driving, correlated with safe driving, results in less braking and therefore better eco-driving behaviour
    The more headway a driver keeps, the less he needs to brake because he has time to anticipate the traffic. Indeed, this figure shows that more headway corresponds with less braking, when comparing the points for one velocity range, e.g. 0-50 km/h. These results are retrieved for one driver after binning the data points per trip in three velocity ranges. Each point represents one trip. We also see that the higher the velocity, the lower the average time headway and the lower the braking energy. Since braking is the main source of energy loss in urban driving, it should be avoided whenever possible by anticipating the traffic situation.

    Supporting Documents

    D45.1 Potential of eco-driving

    The deliverable offers the analysis of possibilities of the naturalistic driving data to provide more insight in different (normal) driving styles and eco-driving. Unique to UDRIVE is the augmentation of the velocity data with driving circumstances, like road type, speed limits, headway, and in-vehicle information. This allows placing the driver behaviour in context and distinguishing personal driving styles from behaviour forced by traffic conditions. To assess the fuel consumption and CO2 emission reduction potential associated with adopting an eco-driving style, it is crucial to separate personal driving style from infrastructure and from congestion while driving.

  • Different driving styles can be identified by different patterns in gear shifting and braking behaviour. The difference in fuel consumption and emissions due to driving behaviour can be up to 25% in urban areas. Anticipating traffic when driving, correlated with safe driving, results in less braking and therefore better eco-driving behaviour
    Since some drivers shift gear much earlier than others, gear shifting behavior is a good indicator for eco-driving behaviour. Drivers that shift gear at low velocities and moderate accelerations enter third gear around 30 km/h, whereas others only reach third gear at 45 km/h in the same type of vehicle.
    In terms of fuel consumption, the lower the engine speed the better, and the eco-driver advice is to change gear between 2000 and 2500 RPM. The average gear shifting RPM for different drivers however ranges from 1400 to 3000 RPM, depending on the vehicle type and the gear, but mostly on the driver behaviour. This large bandwidth means there is quite some room for improvement by better eco-driving behaviour. The estimated difference in fuel consumption due to different engine speeds can be up to 20-25%.

    Supporting documents

    D45.1 Potential of eco-driving

    The deliverable offers the analysis of possibilities of the naturalistic driving data to provide more insight in different (normal) driving styles and eco-driving. Unique to UDRIVE is the augmentation of the velocity data with driving circumstances, like road type, speed limits, headway, and in-vehicle information. This allows placing the driver behaviour in context and distinguishing personal driving styles from behaviour forced by traffic conditions. To assess the fuel consumption and CO2 emission reduction potential associated with adopting an eco-driving style, it is crucial to separate personal driving style from infrastructure and from congestion while driving.

  • Cruise Control is used more often than Speed Limiter but all together they are used only in approx. 10% of the trips. 17% of the equipped drivers don’t even know they have this function. Drivers generally don’t use the 2 functions (they specialize either in  CC use or SL use).The road types where CC and SL are used are equivalent
  • Globaly speaking, 87,3% of the trips are driven seat belt on from start to end. This percentage is different from one country to the other with the lower rate for Poland (76%) and the higher for the Netherlands (95,6%). The most important parameter to explain why subjects drive with seatbelt unlocked is the country. Men have a significantly higher unlocked seat belt rate than women.
    Short trips (about 2 minutes or 325m distance) show a higher proportion of total trip unlocked  and night time is more exposed to full trip unlocked situations.
    Supporting documents

    D42.1 Risk factors, crash causation & normal driving

    This deliverable reports the results of normal and risky driving behaviour. The safety critical event definition section explains the procedure of creating safety critical event triggers (SCE). Actual crashes are very rare, even in a data collection of over 21 months and 200 vehicles. Thus, it is almost impossible to investigate crashes directly. Surrogate measures are used instead to identify and assess potential risk factors. Hard braking, sudden steering, and accelerations are used as surrogates for collisions. While it is reasonable to assume a connection between these surrogates and real crashes, researchers are still uncertain whether SCEs and crashes follow the same patterns. Nonetheless, SCEs are still the best option to investigate how crashes are caused. Additionally to SCEs, episodes with a high relevance to road safety were investigated. On rural roads, more crashes occur than on highways and they are more severe than in cities. This makes them a highly relevant research area in respect to traffic safety.