Development of collision avoidance warning system for heavy vehicles featuring adaptive minimum safe distance / Airul Sharizli Abdullah

Airul Sharizli, Abdullah (2017) Development of collision avoidance warning system for heavy vehicles featuring adaptive minimum safe distance / Airul Sharizli Abdullah. PhD thesis, University of Malaya.

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      The great effort is being recently spent to develop better Collision Avoidance Warning Systems (CAWS) to reduce the total number of accidents. Improvements in crash avoidance have proven far more difficult to attain, mainly because the probability of a crash is affected by an array of complex and interacting factors involving the drivers, vehicles, and the road environment. One of the important criteria in CAWS is the criteria for system activation. Proper activation algorithm of the system will reduce the number of false alarms and collision by not presenting the warning too early or too late, but at the right time. The determination of a minimum safe distance is a very fundamental and pivotal step for CAWS system activation. Hence, the success of CAWS system relies very much on whether the activation algorithm or model used is able to indicate a minimum safe distance precisely and timely. Limitations of existing methods in determining the minimum safe distance is restricted to only the kinematic variables such as speed and deceleration. Other important independent parameters for heavy vehicle such as vehicle classification (VC), Gross Vehicle Weight (GVW) and tire-road coefficient of friction (CoF), which may have a direct impact on vehicle braking performance, have not been explicitly considered. The characteristics of these important heavy vehicle parameters are assumed to be same for all types of vehicle. The minimum safe distance is very much related on vehicle braking performance. The important parameters in vehicle braking performance are the deceleration and braking distance (BD). Thus, this study offers a detailed analysis in understanding the factors which influence the deceleration and BD for heavy vehicle. To do this, deceleration data for various heavy vehicle classes under various loads, speeds and road surface conditions was generated employing a commercial multi-body dynamic simulation package. Through statistical analysis, results shown that these four parameters have significant effect on heavy vehicle’s deceleration and braking distance. The results shows that changes in the vehicle dynamics’ characteristics will affect a heavy vehicle’s braking performance and its ability to stop safely in emergency situations. Therefore these parameters are important and must be considered in determining the minimum safe distance. This result is the first major contribution of this dissertation. To represent the adaptive minimum safe distance which will be used in activation algorithm for CAWS, the new distance-based CAWS model, namely Minimum Safe Distance Gap (MSDG) is introduced. By applying non-linear regression analysis to the simulation results, a mathematical model of MSDG has been established. This MSDG model is the second major contribution of this dissertation. In addition, a graphical user interface (GUI) based calculator was developed based on the proposed regression model. It is envisaged that this calculator would provide a more realistic depiction of the real situation for safety analysis involving heavy vehicles. Finally, the development of prototype microcontroller-based CAWS featuring MSDG activation algorithm has been developed. The accuracy and the functionality of the system has been tested and validated. It is envisaged that this CAWS featuring MSDG algorithm would provide a more realistic depiction of the real traffic situation for safety purposes.

      Item Type: Thesis (PhD)
      Additional Information: Thesis (PhD) – Faculty of Engineering, University of Malaya, 2017.
      Uncontrolled Keywords: Collision avoidance warning system; Heavy vehicles; Minimum Safe Distance Gap (MSDG); Vehicle braking performance; Safety
      Subjects: T Technology > TJ Mechanical engineering and machinery
      Divisions: Faculty of Engineering
      Depositing User: Mr Mohd Safri Tahir
      Date Deposited: 24 Oct 2017 16:37
      Last Modified: 18 Jan 2020 10:12

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