Journal of the Society of Automotive Engineers Malaysia

Side Impact Crashes: Analyzing Vehicle Impact Speed Using Real-World Data

2024-11-20T14:56:16+00:00

Side-impact crashes represent a significant portion of traffic crashes worldwide, often resulting in severe injuries and fatalities. Understanding the dynamics of these collisions, particularly the impact speed of the vehicles involved, is crucial for improving vehicle safety systems and designing effective countermeasures. Additionally, side impact testing for ASEAN NCAP was conducted at a speed of 50 km/h, which is equivalent to Euro NCAP speed testing. This research aims to analyze real-world data collected from side-impact crashes to gain insights into the range of impact speeds and their implications for injury severity. The study utilizes a comprehensive dataset (25 cases) including EDR cases comprising 85 occupants and detailed information from various side impact crash scenarios involving passenger vehicles, including vehicle weight and damage, crash configurations, injury outcomes, and, most importantly, the
outcome of the analysis, which is the impact speeds and crush extent of the vehicle during the crash. By employing advanced statistical (SPSS) and data analysis techniques (AI Damage Software), the key trends and patterns in the data will be identified. These findings shed light on the distribution of impact speeds, crush extent, and their influence on injury severity. From the result, the mean estimated total weight of the striking vehicles involved in side impact crashes is 1,453.08 kg, while the average estimated impact speed of the vehicles involved is 58.96 km/h. Impact speed and occupant exposure were found to be significantly associated with occupant fatalities in side-impact crashes.

Exploring the Relationship between Knowledge of the Child Presence Detection (CPD) System and Intention to Purchase among Adults in Malaysia

2024-11-20T15:02:39+00:00

Ensuring children's safety in cars is crucial, especially considering tragic incidents involving kids left alone in vehicles. Child Presence Detection (CPD) systems offer a solution by alerting caregivers when a child is in the back seat, yet adoption rates are low. This study investigates this relationship, surveying 83 participants on CPD awareness and intent to purchase. The results indicate that 22.2% of respondents who had experienced unattended child cases were aware of CPD systems, compared to 17.9% of those without such experiences. Additionally, 74.1% of respondents with a history of unattended children expressed a willingness to buy CPD systems, compared to 64.3% of those without such experiences. Significant associations were found between CPD knowledge and government employment (X² = 6.245, p = 0.043), suggesting workplace education influences attitudes. However, most demographic factors showed
no significant correlation with CPD knowledge or purchasing intent. Further analysis revealed no significant association between CPD knowledge and intent to purchase, indicating other factors influence buying decisions. This highlights the need for diverse strategies to promote CPD, focusing on benefits, and reducing barriers, and social norms. Future research should explore more factors affecting purchasing behavior to develop effective promotion strategies and improve child safety in cars.

An Analysis of Road Light Intensity on Single Lane Road with Lamp Pole on Curbside Edge

2024-11-20T15:06:39+00:00

This paper provides an in-depth analysis of light intensity on a single-lane road, where lamp poles are positioned at the curb’s edge. The objective is to assess the distribution of light across the road. The study involves measuring light intensity at various heights and angles from the road surface, utilizing a lux meter. For this purpose, a portable streetlamp was specially designed to measure the light intensity on a single-lane road. Several factors were considered, including the road’s length, width, the lamp’s height, and the angle at which the light is projected. Based on these parameters, light intensity readings were obtained along a grid line on the road. The findings reveal that light intensity is higher at the beginning and end of the lamp’s reach, with lower intensity levels observed in the middle of the road between lamp posts. This research provides a valuable foundation for city planners and engineers to improve road lighting systems, thereby enhancing safety and efficiency in transportation networks. Additionally, the study evaluates the potential effectiveness of Autonomous Emergency Braking Systems (AEB) in vehicles.

Evaluation of Differential Steering Performance in VRU Testing Platforms: Accuracy and Reliability Analysis Across Varying Speeds and Circular Trajectories

2024-11-20T15:11:45+00:00

This study investigates the performance of differential steering systems in navigating circular trajectories at varying speeds using a small Vulnerable Road User (VRU) testing platform. The platform, designed with two 600W Brushless Direct Current (BLDC) hub motors and a differential steering mechanism, was evaluated for its accuracy and reliability in following designated paths with radii of 4, 8, and 12 meters. The evaluation was carried out at two motor speeds 5 000 and 10 000 rpm. Significant deviations from the intended path were observed, with deviations ranging from 14.0% to 32.3%, particularly at higher speeds and larger radii. The primary cause of these deviations was identified as wheel slippage on the asphalt surface, worsened by centrifugal forces during turns. Despite these challenges, the steering system demonstrated good reliability, maintaining deviations within 10%. The study underscores the need for optimizing wheel materials and refining control algorithms to reduce slippage and improve maneuverability. These findings offer valuable insights into enhancing the accuracy of differential steering systems in VRU testing platforms, contributing to the development of more effective Advanced Driver-Assistance Systems (ADAS).

A Comparative Study on Used Car Price Prediction Model

2024-11-20T15:14:45+00:00

The number of used car prices in the market keeps increasing due to the launch of a new model by the car manufacturer. The sales price is determined by the car's specifications and present state. The objective of this study is to make a comparison of the machine learning algorithm that can be implemented for used car price prediction. Previous studies on used car price prediction commonly perform the comparison of the machine learning model, meanwhile, the study on stock price prediction utilizes hyperparameter tuning. It shows that hyperparameter tuning can increase the performance of the machine learning model. The expected outcome from the study is the
best machine learning model will be used for used car price prediction. The machine learning model will be trained by using Azure Machine Learning Studio. Therefore, the study compares four different machine learning models, including linear regression, neural network regression, boosted decision tree regression, and decision forest regression. As a result, boosted decision tree regression is indicated as the most effective model, exhibiting high R-squared values and superior performance compared to the other machine learning algorithms. This study also will perform a comparative study on the prediction model with hyperparameter tuning to get the most feasible and accurate model for the prediction model. There are two different hyperparameter tuning being compared such as the entire grid and random sweep and the study shows that random sweep provides the best R-squared values at 0.874548. The best machine learning model will be deployed and integrated with the web application that is developed by using ASP.NET. This study will be beneficial to the community in providing valuable insight into the factors that influence used car prices. These insights can be utilized by industry professionals and market analysts to make informed decisions and develop effective strategies.

Profiling of Child Restraint System (CRS) in the ASEAN Market

2024-11-20T15:17:24+00:00

Child Restraint Systems (CRS) are designed for child safety while traveling in vehicles. Accordingly, appropriate CRS is of interest because of its effect on the safety performance of the seat. In ASEAN, the New Car Assessment Program for Southeast Asian Countries (ASEAN NCAP) includes assessments of CRS fitment in the newly tested vehicle model designs. However, there has been no detailed investigation
of the CRS availability on the road, despite the dynamic CRS market. Therefore, this study aims to establish a list of the most popular UN R44 or R129 certified CRS in four major ASEAN automotive hub countries, i.e. Malaysia, Indonesia, Thailand, and Vietnam. A market survey was conducted through physical and online platforms to identify the CRS details. The list known as the CRS Reference List was finalized according to the CRS models' availability in at least three ASEAN markets and category variability for the list. This effort is crucial to ensure the CRS reference list remains relevant for the ASEAN NCAP assessment. Thus far, the suggested list supports an effective child occupant injury prevention program that suits a local situation.