Sensor Data Fusion Based Estimation of Tyre-Road Friction to Enhance Collision Avoidance: Dissertation

    Research output: ThesisDissertationCollection of Articles

    1 Citation (Scopus)

    Abstract

    Vehicle steering, braking and acceleration are subject to friction forces arising from contact of the tyres with the road surface. The contact force is both enabling and limiting. The ratio of the contact friction to the force of the tyres pressing on the road surface is described as the coefficient of friction. The maximum coefficient of friction for different surfaces characterizes the extent of tyre grip. Collision avoidance and collision mitigation systems require information on tyre grip so as to accurately calculate braking distances and evasive manoeuvres. Estimating road slipperiness (skid resistance) during driving has however proven difficult. This dissertation discusses estimating the maximum coefficient of friction (herein referred to as the friction potential) together with determining road conditions. Both estimations are based on multi-sensor data fusion; that is, combining data from several sensors. The presented sensor data fusion utilizes various sensors from three main classes: 1) environmental sensors, 2) sensors measuring vehicle dynamics and 3) experimental tyre sensors. This work concentrates particularly on methods for combining measurements of vehicle dynamics with environmental sensor readings; for example, wheel speed signals are linked to readings about ice, snow or water on the road. The methods were incorporated into a prototype passenger car implementation, where testing yielded a reliable estimate of friction potential for approximately 90% of driving time. The estimate of friction potential was then within 0.2 of reference values measured in braking tests. These results encapsulate a proof of concept on asphalt roads in some wet, snowy, icy and dry road conditions. The advantages of friction estimation for collision avoidance and collision mitigation systems are analysed using mainly simulations. A correct initial estimate of the friction potential enables the systems to improve traffic safety efficiently also in slippery road conditions. However, the range of available environmental sensors does not cover long braking distances. Together with the simulations, the work introduces a new method for collision avoidance calculations and timing the activation of collision mitigation. The method is based on a large number of pre-calculated vehicle trajectories.
    Original languageEnglish
    QualificationDoctor Degree
    Awarding Institution
    • Tampere University of Technology (TUT)
    Supervisors/Advisors
    • Huhtala, Kalevi, Supervisor, External person
    Award date12 Mar 2010
    Place of PublicationEspoo
    Publisher
    Print ISBNs978-951-38-7382-0
    Electronic ISBNs978-951-38-7383-7
    Publication statusPublished - 2010
    MoE publication typeG5 Doctoral dissertation (article)

    Fingerprint

    Sensor data fusion
    Collision avoidance
    Tires
    Friction
    Braking
    Sensors
    Skid resistance
    Passenger cars
    Snow
    Asphalt
    Ice
    Wheels
    Information systems
    Chemical activation
    Trajectories

    Keywords

    • friction
    • sensor
    • data fusion
    • collision avoidance
    • collision mitigation
    • environmental sensing
    • tyre
    • road
    • conditions
    • trajectory
    • curvature-velocity
    • ADAS

    Cite this

    @phdthesis{079c3c4e65bb4d6bb4c8efa61916833c,
    title = "Sensor Data Fusion Based Estimation of Tyre-Road Friction to Enhance Collision Avoidance: Dissertation",
    abstract = "Vehicle steering, braking and acceleration are subject to friction forces arising from contact of the tyres with the road surface. The contact force is both enabling and limiting. The ratio of the contact friction to the force of the tyres pressing on the road surface is described as the coefficient of friction. The maximum coefficient of friction for different surfaces characterizes the extent of tyre grip. Collision avoidance and collision mitigation systems require information on tyre grip so as to accurately calculate braking distances and evasive manoeuvres. Estimating road slipperiness (skid resistance) during driving has however proven difficult. This dissertation discusses estimating the maximum coefficient of friction (herein referred to as the friction potential) together with determining road conditions. Both estimations are based on multi-sensor data fusion; that is, combining data from several sensors. The presented sensor data fusion utilizes various sensors from three main classes: 1) environmental sensors, 2) sensors measuring vehicle dynamics and 3) experimental tyre sensors. This work concentrates particularly on methods for combining measurements of vehicle dynamics with environmental sensor readings; for example, wheel speed signals are linked to readings about ice, snow or water on the road. The methods were incorporated into a prototype passenger car implementation, where testing yielded a reliable estimate of friction potential for approximately 90{\%} of driving time. The estimate of friction potential was then within 0.2 of reference values measured in braking tests. These results encapsulate a proof of concept on asphalt roads in some wet, snowy, icy and dry road conditions. The advantages of friction estimation for collision avoidance and collision mitigation systems are analysed using mainly simulations. A correct initial estimate of the friction potential enables the systems to improve traffic safety efficiently also in slippery road conditions. However, the range of available environmental sensors does not cover long braking distances. Together with the simulations, the work introduces a new method for collision avoidance calculations and timing the activation of collision mitigation. The method is based on a large number of pre-calculated vehicle trajectories.",
    keywords = "friction, sensor, data fusion, collision avoidance, collision mitigation, environmental sensing, tyre, road, conditions, trajectory, curvature-velocity, ADAS",
    author = "Sami Koskinen",
    note = "Project code: 4066",
    year = "2010",
    language = "English",
    isbn = "978-951-38-7382-0",
    series = "VTT Publications",
    publisher = "VTT Technical Research Centre of Finland",
    number = "730",
    address = "Finland",
    school = "Tampere University of Technology (TUT)",

    }

    Sensor Data Fusion Based Estimation of Tyre-Road Friction to Enhance Collision Avoidance : Dissertation. / Koskinen, Sami.

    Espoo : VTT Technical Research Centre of Finland, 2010. 209 p.

    Research output: ThesisDissertationCollection of Articles

    TY - THES

    T1 - Sensor Data Fusion Based Estimation of Tyre-Road Friction to Enhance Collision Avoidance

    T2 - Dissertation

    AU - Koskinen, Sami

    N1 - Project code: 4066

    PY - 2010

    Y1 - 2010

    N2 - Vehicle steering, braking and acceleration are subject to friction forces arising from contact of the tyres with the road surface. The contact force is both enabling and limiting. The ratio of the contact friction to the force of the tyres pressing on the road surface is described as the coefficient of friction. The maximum coefficient of friction for different surfaces characterizes the extent of tyre grip. Collision avoidance and collision mitigation systems require information on tyre grip so as to accurately calculate braking distances and evasive manoeuvres. Estimating road slipperiness (skid resistance) during driving has however proven difficult. This dissertation discusses estimating the maximum coefficient of friction (herein referred to as the friction potential) together with determining road conditions. Both estimations are based on multi-sensor data fusion; that is, combining data from several sensors. The presented sensor data fusion utilizes various sensors from three main classes: 1) environmental sensors, 2) sensors measuring vehicle dynamics and 3) experimental tyre sensors. This work concentrates particularly on methods for combining measurements of vehicle dynamics with environmental sensor readings; for example, wheel speed signals are linked to readings about ice, snow or water on the road. The methods were incorporated into a prototype passenger car implementation, where testing yielded a reliable estimate of friction potential for approximately 90% of driving time. The estimate of friction potential was then within 0.2 of reference values measured in braking tests. These results encapsulate a proof of concept on asphalt roads in some wet, snowy, icy and dry road conditions. The advantages of friction estimation for collision avoidance and collision mitigation systems are analysed using mainly simulations. A correct initial estimate of the friction potential enables the systems to improve traffic safety efficiently also in slippery road conditions. However, the range of available environmental sensors does not cover long braking distances. Together with the simulations, the work introduces a new method for collision avoidance calculations and timing the activation of collision mitigation. The method is based on a large number of pre-calculated vehicle trajectories.

    AB - Vehicle steering, braking and acceleration are subject to friction forces arising from contact of the tyres with the road surface. The contact force is both enabling and limiting. The ratio of the contact friction to the force of the tyres pressing on the road surface is described as the coefficient of friction. The maximum coefficient of friction for different surfaces characterizes the extent of tyre grip. Collision avoidance and collision mitigation systems require information on tyre grip so as to accurately calculate braking distances and evasive manoeuvres. Estimating road slipperiness (skid resistance) during driving has however proven difficult. This dissertation discusses estimating the maximum coefficient of friction (herein referred to as the friction potential) together with determining road conditions. Both estimations are based on multi-sensor data fusion; that is, combining data from several sensors. The presented sensor data fusion utilizes various sensors from three main classes: 1) environmental sensors, 2) sensors measuring vehicle dynamics and 3) experimental tyre sensors. This work concentrates particularly on methods for combining measurements of vehicle dynamics with environmental sensor readings; for example, wheel speed signals are linked to readings about ice, snow or water on the road. The methods were incorporated into a prototype passenger car implementation, where testing yielded a reliable estimate of friction potential for approximately 90% of driving time. The estimate of friction potential was then within 0.2 of reference values measured in braking tests. These results encapsulate a proof of concept on asphalt roads in some wet, snowy, icy and dry road conditions. The advantages of friction estimation for collision avoidance and collision mitigation systems are analysed using mainly simulations. A correct initial estimate of the friction potential enables the systems to improve traffic safety efficiently also in slippery road conditions. However, the range of available environmental sensors does not cover long braking distances. Together with the simulations, the work introduces a new method for collision avoidance calculations and timing the activation of collision mitigation. The method is based on a large number of pre-calculated vehicle trajectories.

    KW - friction

    KW - sensor

    KW - data fusion

    KW - collision avoidance

    KW - collision mitigation

    KW - environmental sensing

    KW - tyre

    KW - road

    KW - conditions

    KW - trajectory

    KW - curvature-velocity

    KW - ADAS

    M3 - Dissertation

    SN - 978-951-38-7382-0

    T3 - VTT Publications

    PB - VTT Technical Research Centre of Finland

    CY - Espoo

    ER -