Universiti malaysia pahang autonomous shuttle Development : Lane classification analysis using convolutional neural network (CNN)

In recent years, the widespread adoption of autonomous vehicle, advanced driver assistant systems (ADAS) have acquired great interests as it provides safe and better driving by automating, adapting, and enhancing the driving experience. Road accidents can be avoided with the identification of various road infrastructures such as merging or splitting lanes as well as ending lanes must be well detected, providing a driver with a more convenient and safe intelligent function. However, the image for lane detection failed to be detected due to the visibility of image is affected because it may consist of noise, occlusion, undesired background blur and the image pixels. To predict the lane markers on road pavement correctly, a robust lane classification system using deep learning approach requires guidance so that it can detect significantly. Four significant operations involve in developing the system which are data acquisition, data pre-processing, data training and data testing. In this study, an improved classification algorithm using deep learning specifically convolutional neural network is used to detect the lane markers. The big dataset consists of 5000 images. It is distributed into are 4000 images as training data, 700 images as validation data and 300 images as testing data respectively. For the evaluation of lane detection system, the evaluation metrics are in terms of accuracy, false positive (FP) and false negative (FN). The accuracy of the lane classification system network is 91.97%

A bezier curve optimization method based on segmentation factor (t) for path planning in autonomous navigation

Path planning plays a major role in autonomous navigation. Different curve fitting methods are used in creating path for autonomous vehicles. Among those Bezier curves are widely used to create path especially in roundabouts due to their special properties. The primary factor that affects the shape and curvature of the Bezier curve is the control points. Researchers have established equations for finding the control points of a Bezier curve passing through selected points. The two critical elements that determine the control points are the random points selected for control point calculation and the segmentation factor

A comprehensive review on different path planning methods for autonomous vehicles

Autonomous vehicle is an active field where researches are going on to improve the vehicle's capability to travel autonomously from one place to another. Vehicle has to progress through different levels of control structure to navigate through different environments. Among those path planning plays a major role in autonomous vehicles navigation as different planning methods need to be used for planning the path at different intersections for the vehicle. However, AVs still face some challenges in urban intersections such as roundabouts, obstacle avoidance, which need to be resolved for completely automated path planning in AVs. So, this paper presents an overview on different path planning methods implemented in autonomous navigation. A description on different path planning methods and implementation of these methods by different authors is presented

Development of a smart sensing unit for LoRaWAN-based IoT flood monitoring and warning system in catchment areas

This study introduces a novel flood monitoring and warning system (FMWS) that leverages the capabilities of long-range wide area networks (LoRaWAN) to maintain extensive network connectivity, consume minimal power, and utilize low data transmission rates. We developed a new algorithm to measure and monitor flood levels and rate changes effectively. The innovative, cost-effective, and user-friendly FMWS employs an HC-SR04 ultrasonic sensor with an Arduino microcontroller to measure flood levels and determine their status. Real-time data regarding flood levels and associated risk levels (safe, alert, cautious, or dangerous) are updated on The Things Network and integrated into TagoIO and ThingSpeak IoT platforms through a custom-built LoRaWAN gateway. The solar-powered system functions as a stand-alone beacon, notifying individuals and authorities of changing conditions. Consequently, the proposed LoRaWAN-based FMWS gathers information from catchment areas according to water level risks, triggering early flood warnings and sending them to authorities and residents via the mobile application and multiple web-based dashboards for proactive measures. The system's effectiveness and functionality are demonstrated through real-life implementation. Additionally, we evaluated the performance of the LoRa/LoRaWAN communication interface in terms of RSSI, SNR, PDR, and delay for two spreading factors (SF7 and SF12). The system's design allows for future expansion, enabling simultaneous data reporting from multiple sensor monitoring units to a server via a central gateway as a network

The normal vehicle forces effects of a two in-wheel electric vehicle towards the human brain on different road profile maneuver

Noise, harshness and vibrations are a non-trivial aspect of ride or human comfort, and car manufacturers often sought to improve the aforesaid comfort level. In previous studies, human biodynamic model and vehicle model are often modelled separately. Human model is used to study human alertness level and health while vehicle model is used to study on the car vibration to specifically understand the impact of vibration towards the model independently. In this study, a twelve degrees of freedom (12 DOF) human biodynamic model is incorporated with a two in-wheel electric car model to investigate the effect of vertical vibration towards the human brain based on different types of road profile and maneuver. MATLAB simulation environment is used to carry out the investigation, and it was established from the present study that the proposed model is able to provide significant insights on the impact experienced by the human brain to the skull based on the given vertical input of different road profile. The impact on the human brain to the skull is often associated with human alertness while driving where vibration exposure towards human driver influence the sleepiness level, human reaction times and lapses of attention which may lead to road accidents

Preventive maintenance data logger monitoring system

This research presents the Preventive Maintenance Data Logger (PMDL) Monitoring System and the process of how it has been manufactured. Preventive Maintenance Data Logger Monitoring System is a device which will collect the data from vehicle’s sensor for prevention maintenance and then save the data to other storage for future analysis. Preventive Maintenance Data Logger Monitoring System also can send notification to user for crash prevention. This project comprises of mechanical system, electronic system, and software system. The methodology of the Preventive Maintenance Data Logger system and prototype development is discussed in this paper on the manufacturing processes. The software is programmed using C language in Arduino software and the notification for preventive are develop using BLYNK application. Manufacturing processes involves in making this project, including additive manufacturing, welding and cutting. Several test case studies were conducted to verify the capability of the device in term of the vehicle speed, location, crash point data, distance between other vehicles detection and reliability

Outdoor position estimation of a mobile platform for precision farming and agriculture automation

Precision farming is a topic that is gaining attention due to its potential to increase efficiency and reduce labor workload in the agriculture industry. Automation using mobile robots is expected to revolutionize the industry. however, a few technical challenges remain. This research is a test of a low-cost position sensor for the application of position estimation of a mobile robot. The objective is to measure the error produced by an ultra-wideband position sensor and to develop a mobile robot system using the Robot Operating System (ROS). The error generated is analyzed based on the measurements taken in outdoor and indoor experiment environment settings. The Root Mean Squared error method was performed to evaluate the performance of the position sensor. The result of the experiment shows that the installation layout of the sensors and the surrounding environment affects the error generated. It is concluded that the low-cost sensor has sufficient reliability for use in the agriculture setting

Investigation of the combination of kinematic path planning and artificial potential field path planning with PI controller for autonomous emergency braking pedestrian (AEB-P) System

Autonomous Emergency Braking Pedestrian (AEB-P) is a fundamental capacity of the advanced driver assistance system (ADAS) to maintain a distance and avoid a collision. The fatality of pedestrian in traffic accident as well as near-miss accidents are the most frequent type of accidents in Malaysia as the improvisation of AEB-P system are obligatory. To generate optimum vehicle deceleration from the path planner in the presence of a pedestrian in front of the vehicle, an Artificial Potential Field (APF) path planner with a kinematic path planner is proposed in this research. The kinematic path planner will produce maximum deceleration for the vehicle, 8 m/s2, as the vehicle violates the threshold. The value is combining with the APF value to fetch to the PI controller. Thus, the AEB-P system was designed considering the pedestrian walked in front of the vehicle at 4.32 km/h and vehicle travelled at 60 km/h, dry and wet road surface condition, time for Front Collision Warning (FCW), and full braking was included for the limit APF is developed. The PI controller will tune the deceleration using its variable on dry road surface (P = 0.003, I = 5) and on wet road surface (P = 0.003, I = 8500). The host vehicle starts to give warning signal at 37.29 m and experience full braking at 21.3 m when the vehicle travel on both types of surfaces. The vehicle manages to stop from hitting the pedestrian at 2.21 and 1.5 m on the dry and wet road surface. The proposed AEB-P architecture can avoid the collision with pedestrian as the vehicle manage to stop from hitting the obstacle at a comfortable distance

Investigation of brake pad wear impact on autonomous emergency braking pedestrian performance on wet road conditions

This study presents an investigation of autonomous emergency braking pedestrian (AEB-P) system performance during harsh braking on wet road pavement. The system was designed to consider a pedestrian walking in front of the host vehicle. The performance of the AEB-P system would degrade immediately as the pads on the brakes become worn, and the vehicle continues to brake on a wet road surface. The vehicle conditional artificial potential field (VC-APF) is an innovative approach for motion planning in the AEB-P introduced in this work. The simulation was performed to explore the impact of brake pad degradation on VC-APF effectiveness on wet road pavement. The first evaluation involved a test to evaluate the effectiveness of the risk assessment in the AEB-P system when encountering a moving obstacle (pedestrian). The second test evaluated VC-APF performance, for instance, the vehicle's safety distance when the vehicle performed hard braking at 0.4, 0.35, and 0.24 brake pad friction coefficients. The third evaluation focused on the vehicle’s speed behavior during deceleration at various brake pad friction coefficients. The simulation results showed that while braking at 0.4 and 0.35 brake pad friction coefficients, the vehicle maintained a minimum safety distance of 1.5 m and 0.69 m from a pedestrian on wet road pavement, respectively. However, the brake pad friction coefficient of 0.24 failed to prevent the vehicle from crashing. The findings indicate that an exhausted brake pad reduces the vehicle's safety