Ahmad Saifizul Abdullah

A new multi-objective optimization method for full-vehicle suspension systems

The conventional approach in vehicle suspension optimization based on the ride comfort and the handling performance requires decomposition of the multi-performance targets, followed by lengthy iteration processes. Suspension tuning is a time-consuming process, which often requires the benchmarking of competitors’ vehicles to define the performance targets of the desired vehicle by experimental techniques. Optimum targets are difficult to derive from benchmark vehicles as each vehicle has its own unique vehicle set-up. A new method is proposed to simplify this process and to reduce significantly the development process. These design objectives are formulated into a multi-objective optimization problem together with the suspension packaging dimensions as the design constraints. This is in order to produce a Pareto front of an optimized vehicle at the early stages of design. These objectives are minimized using a multi-objective optimization workflow, which involves a sampling technique, and a regularity-model-based multi-objective estimation of the distribution algorithm to solve greater than 100-dimensional spaces of the design parameters by the software-in-the-loop optimization process. The methodology showed promising results in optimizing a full-vehicle suspension design based on the ride comfort and the handling performance, in comparison with the conventional approach.

Development of real-time lane detection software for automated steering system

In recent years, there has been a growing interest in the development of Intelligent Transportation System (ITS), providing safety and comfort to passengers, and thereby decreasing the number of accidents. In addition, the efficiency of highway utilization can be increased tremendously by replacing some of the basis tasks of driving such as accelerating to a pre-specified velocity, braking within a safe distance, and automatically controlling the steering movement of the car within the left and right lanes with the help of intelligent vehicle control systems. One of the intelligent vehicle control system components is the automated steering system. Generally, the automated steering system can be further categorized into two major subsystems, the route detection and the control system. The development of these subsystems involves multi-disciplined areas such as instrumentation, signal and image processing, mathematical modeling, and control engineering. There are two approaches in developing the route detection system, the look-down and the look-ahead. This research focuses on the development of the route detection system based on the look-ahead approach where a machine vision system is used as the detector for the automated steering system to extract lane information.

Simulation and Analysis on the Effect of Gross Vehicle Weight on Braking Distance of Heavy Vehicle

Increasing number of fatalities caused by road accidents involving heavy vehicles every year has raised the level of concern and awareness on road safety situation in developing countries like Malaysia. This study attempts to explore the influences of vehicle dynamics characteristics such as vehicle weight and travel speed on its safety braking distance. This study uses a kind of complex virtual prototyping software to simulate vehicle dynamics and its braking performance characteristics. The software was used to generate braking distance data for various vehicle types under various loads and speed condition. The generated data was grouped according to GVW and then analyzed by two-way ANOVA to evaluate its relationship to braking distance. The finding of this study implies that the speed and GVW of various vehicle classifications has a significant effect to the heavy vehicle braking distance.