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 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

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

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

Investigating vehicle characteristics behaviour for roundabout cornering

The allowable range of speed that a vehicle can tolerate in a constant radius turn is crucial for the development of smart assistance systems. Although the development of advanced system observers has been grown since early days of its introduction, extensive study is required in monitoring the vehicle’s behaviour in the conditions such as variation of vehicle dynamic parameters and terrain type. Autonomous vehicles will fail to judge the parameter of the road cornering due to the safety constraints of the vehicle. Thus, the primary concern of this paper is to study the vehicle’s behaviour for different curvature profiles. A real-time simulation for a typical Sedan is presented to test a constant roundabout turning with a radius of 50 m for this measure. In prior to that, a detailed analysis on the vehicle stability and handling responses are discussed. The vehicle is found to be traveling in a stable region at a speed from 10 to 74 km/h. The vehicle enters a critical area when speed is more than 74 km/h. Therefore, that the allowable range of speed that the vehicle can travel in a 50 m radius turn lies between 10 to 74 km/h. The stability is evaluated by two criterions which are the yaw rate and sideslip angle

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

Object tracking for autonomous vehicle using YOLO V3

Accuracy and performance of an object detection model have always been the main requirements for an object tracking system. In this project, the performance of machine learning based object detection using YOLO v3 technique will be investigated. Two models were provided where one model is trained using online Common Objects in Contact (COCO) dataset only, and the other model is trained with additional images from Universiti Malaysia Pahang (UMP) with several different locations dataset. The performance of the trained models were evaluated using mean Average Precision (mAP), and precision techniques. The model with highest precision was selected to be implemented on actual road test. The results show that the model 2 has the highest precision and was able to detect every class of objects. Each output box had displayed the class and the distance to the objects from the RGBD camera of the vehicle. It is observed that the first model that was trained to perform the mAP value of 90.2% and a performance of 0.484 precision. For the second model, it can be seen that the accuracy of the detections are higher than that of model 1. Therefore, model 2 has a better performance with a value of 0.596 precision

A Novel Triangular-Based Estimation Technique for Bezier Curve Control Points Generation on Autonomous Vehicle Path Planning at the Roundabout Intersection

Path planning plays a major role in autonomous vehicle navigation. Among different kinds of intersections, roundabouts are far more difficult to plot a course than other intersections due to their special design. Different curve fitting methods such as circles, clothoid curves, Bezier curves are utilized for path planning inside a roundabout. Among those Bezier curves are widely used as they can generate a possible number of paths. However, the major drawback when adopting a Bezier curve is locating its control points. Control points need to be placed correctly for the path to generate. Roundabouts have varying shapes and sizes, there is no one-size-fits-all strategy for locating the control points in a roundabout. Moreover, the circulating path of a roundabout is generally formed based on the radius of the circular path, but this method cannot be applied in all roundabouts since the roundabout can have variable shapes depending on the available space. Therefore, this paper introduces a new method called the triangular-based point selection approach for locating the control points for a Bezier curve traversing a path. The triangular-based point selection approach is used to find points on the road to calculate the control points of a Bezier curve passing through these points. The circular path is likewise created using Bezier curve allowing the path to follow the contour of the roundabout. The proposed method is demonstrated in an oval-shaped roundabout and tested using a vehicle model also the proposed path generation algorithm is compared with other path planning methods