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

Estimation of electric vehicle turning radius through machine learning for roundabout cornering

This paper presents an alternative approach for estimating the turning radius using machine learning technique. While on-board sensors are unable to offer adequate information on vehicle states to the algorithm, vehicle states other than those directly detected by on-board sensors can be inferred using machine learning (ML) approaches based on the collected data. A compact electric vehicle model is used to obtain data and measurements of the vehicle states for different sets of road radius. The augmented basic measurements is fed to an Extra Tree Regression to predict the turning radius of the vehicle. The feasibility of the developed algorithm was tested and validated using performance metrics. The results show that the regression accuracy for the turning radius is 99% and can be obtained with sufficient vehicle dynamics information

Normal forces effects of a two in-wheel electric vehicle towards the human body

Traditionally, in order to comprehend the impact of vibration on human and vehicle ride comfort, past research often models the human biodynamic and vehicle models individually. Recent trends suggest that a better understanding of the behaviour could be achieved by fusing the models instead of analysing it separately. The present study evaluates the impact of the normal forces on specific parts of the human body. A human biodynamic model with five degrees of freedom is modelled together with a two in-wheel electric car model travelling at a speed of 10 km/h to investigate the effect of the normal forces. From the present investigation, it could be observed that the proposed model could highlight the impact of the normal forces on the body parts when the car is travelling either on a straight path or in taking corners

Rain classification for autonomous vehicle navigation using machine learning

Autonomous vehicles (AV) has gained popularity in research and development in many countries due to the advancement of sensor technology that is used in the AV system. Despite that, sensing and perceiving in harsh weather conditions has been an issue in this modern sensor technology as it needs the ability to adapt to human behaviour in various situations. This paper aims to classify clear and rainy weather using a physical-based simulator to imitate the real-world environment which consists of roads, vehicles, and buildings. The real-world environment was constructed in a physical-based simulator to publish the data logging and testing using the ROS network. Point cloud data generated from LiDAR with a different frame of different weather are to be coupled with three machine learning models namely Naïve Bayes (NB), Random Forest (RF), and k-Nearest Neighbour (kNN) as classifiers. The preliminary analysis demonstrated that with the proposed methodology, the RF machine learning model attained a test classification accuracy (CA) of 99.9% on the test dataset, followed by kNN with a test CA of 99.4% and NB at 92.4%. Therefore, the proposed strategy has the potential to classify clear and rainy weather that provides objective-based judgement

Implementation of platooning strategy for autonomous mobile robot

Current studies show automated mobile robot is being widely researched to reduce human work and ease the job. Hazards and human limitations have been the main reason for the demand for a more versatile and robust mobile robot especially in machining industries and military base. However, still often require this kind of robot as assisting equipment in public places such as shopping mall, airport to help senior citizens and disabled people who are unable to carrying things with them when they move around. In this project, an automated mobile robot was developed perpendicularly with proposing platooning system to be used. The model of the platooning strategy analyzed via simulation and experiments. The robot was built together with myRIO as main controller. The Kinect was used for human detection and acquiring distance data. MATLAB was used as interface to send the distance data from the Kinect to myRIO using UDP. Results found that the automated mobile robot successfully detect and follow human to the target location while carrying some objects. It is believed that this robot can reduce the burden of human especially senior citizen and disabled people when carrying heavy things in public places. Moreover, this robot also can contribute more in completing heavy task in human daily life

Effect of unbalanced overloading on the cornering stability profile of nonholonomic two in-wheel compact electric vehicle

The recent development of vehicle technology is shifting towards the autonomous and electric vehicle. Electric vehicle technology has grown to pave a path towards wheel motored electric vehicles (IWMEV). Like conventional internal combustion vehicle, IWMEV are also susceptible to instability which could result in accidents. Accidents are divided into three categories based on the cause, namely vehicle condition, human error and environmental condition. Most accidents that occur are results of human behaviour. Unbalanced overloading is identified as one of the factors that affect the stability of the vehicle thus, leading to accidents. Increasing load on one side of the vehicle moves the position of the centre of gravity leading to an increase in the probability of vehicle instability. Moreover, compared to conventional internal combustion vehicle, IWMEV are considered lightweight vehicle due to the absence of mechanical linkage and engine. This causes IWMEVs to be affected by unbalanced overloading. Therefore, the objective of this research is to identify the effect of unbalanced overloading on the stability profile of the electric vehicle. Thus, a simulation model of an IWMEV is developed by combining the load transfer equation, Dugoff’s tire model, nonlinear vehicle dynamic equation and the DC motor model. The developed model is verified using a compact IWMEV. Then, the model is used to identify the effect of load increase at the left and right side during a sharp right turn. The vehicle is set to run at four different velocities namely 10 km/h, 15 km/h, 20 km/h and 25 km/h. It is observed that the vehicle reaches the Friction Circle Coefficient limit at the front left tire for a 60% right load increase condition. This causes the vehicle to crash. A load stability index named Binary Attribute Stability Indicator (BASI) is proposed to identify the stability of the vehicle at different load distribution. The BASI can help determine the stability level of the vehicle based on lateral acceleration, yaw rate, FCC, and rollover index

Platooning Strategy of Mobile Robot: Simulation and Experiment

Concurrent studies show vehicle platooning system as a promising approach for a new transportation system. The platooning strategy can be also applied to automated mobile robots. Including dynamic modelling in the simulation with kinematic model would yield a different result as the dynamic modelling would include the physical parameters of the mobile robot. The aim is to create a model that describes the motion of a robot that follows another robot based on predetermined distance. Dynamic model of the proposed mobile robot is simulated and the kinematic modelling was included in to simulate the motion of the mobile robot. PID controller will be used as a controller for robot’s motion and platooning strategy. A reference distance is given as the input and the PID controller computes the error and sends input to the mobile robot in the form of voltage. The robot is able to follow the leader robot by maintaining a distance of one metre with a small deviation in the direction as the robot tends to move towards the left due to forces acting on the wheel. This method can be implemented in a human following mobile robot where the leader robot is replaced with a human user

Effect of road profile on normal force generated on electric vehicle

Electric vehicles are gaining popularity for its various advantages including environmental aspects. However, the vehicles are still susceptible to accidents due to factors such as uneven road surface. Thus, this paper focus on the effect of road profile on the suspension and normal force produced on an electric vehicle. A simple vehicle model is designed in MATLAB Simulink using longitudinal vehicle dynamic model and passive suspension of the quarter-car model. The vehicle is accelerated on the road while introducing an uneven road surface. The result obtained shows an increase of the vehicle suspension deflection and normal force produced. A vehicle moving on three varying hump height is shown to produce a minor disturbance on the total normal force of the vehicle. However, the effect is significant enough on the normal force on each tire