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The majority of technological processes include dynamic systems, which are characterized by structural and parameter uncertainty. Conventional control procedures based on such models are unlikely to produce the desired performance since deterministic models cannot appropriately characterize these uncertainties. When dealing with complex nonlinear systems that are characterized by ill-defined and unknown elements, fuzzy-logic based system is an effective method. Fuzzy controllers—whose rule basis is built on the expertise of human experts—are frequently utilized in this context to achieve desired performance. This knowledge might not be adequate for some complex processes, thus different methods for creating IF-THEN rules have been suggested. In this paper, the design of higher-order fuzzy sets is discussed and a generalized novel algorithm is developed for implementing type-n fuzzy sets and it is applied for controlling the angle of steering for a vehicle in such a way that the vehicle tracks the desired path. In the proposed methodology, fuzzification and defuzzification are taking place n number of times, and defuzzification of higher order fuzzy values up to type-1 fuzzy set is performed using the center of gravity method. The obtained type-1 fuzzy values are used in the rule base and inference mechanism for calculating the final crisp output. The significance of the present research is that the computation cost is not as high as expected because both fuzzification and defuzzification processes are recursive and the reduced new type-1 fuzzy values have the additional ability to model the uncertainty and vagueness as compared to normal type-1 fuzzy values. A comparison of the controllers based on the type of fuzzy sets is done based on the square of error (SE). Both the disturbance signal and uncertainty scenarios were also considered. The use of higher-order fuzzy sets facilitates the controller to give the more accurate results for controlling the vehicle and the corresponding simulation results suggest the same. [ABSTRACT FROM AUTHOR]

Steering angle controller is a core module of autonomous vehicles, where a slight miscalculation can cause severe accidents. Following safety precautions, development of robust and precise steering angle predictor is an active area of research. However, existing simulator based steering angle predictors lack in predictive performance and have not been evaluated on same benchmark datasets. Furthermore, most of them are evaluated on simulated datasets and their potential on real-world data as well as in cross-domain evaluation of both types data (real-world, simulated) remain unexplored. To accelerate and expedite research related to steering angle prediction contributions of this paper are manifold: 1) It presents two benchmark datasets that are developed using Udacity and CARLA simulators. 2) Following the need for comparative study, over both simulated datasets, it benchmarks the performance of existing predictors under 2 different evaluation settings namely: same-track and cross-track evaluation. 3) In cross-domain evaluation, it explores generalization potential of predictors by training predictors on simulated data and evaluating them on 2 real-world datasets and vice versa. 4) It presents a robust and precise steering angle predictor that utilizes skip connections for proper gradient flow among different convolutional layers. In same-track evaluation where predictors are trained and evaluated on same-track data, proposed predictor outperforms existing predictors by achieving least Mean Absolute Error (MAE) of 0.13, 0.19 and 0.065 over lake track, jungle track and CARLA based datasets, respectively. Similarly, in cross-track evaluation where predictors are trained on one track and are evaluated on other track data, once again proposed predictor outperforms existing predictors by producing average least MAE errors of 0.33 and 0.06 over Udacity and CARLA datasets, respectively. Over two real-world datasets, Sully Chen and Comma.ai, the proposed predictor demonstrates superior performance compared to existing simulator-based predictors, achieving the lowest MAE of 2.41 and 0.50, respectively.

The technology for determining the location of an object on the ground is the foundation for building vehicle navigation systems and surveillance systems after them. In general, such navigation systems make it possible to determine the coordinates of the vehicle's location and its directional angle, find the direction to the destination, and calculate the coordinates of observation objects. The technical implementation of systems can be quite diverse - from implementation on mechanical elements to their implementation using microelectronics elements. The main factors in the development of navigation systems are the transition to computer-integrated systems and resource-saving technologies. This will make it possible to increase the accuracy of navigation information, obtain a significant gain in the weight and size characteristics of the equipment, reduce its energy consumption and increase reliability. The possibility of modernization of odometrical navigation systems based on integrated computer technologies is considered. [ABSTRACT FROM AUTHOR]

The electro-hydraulic actuator (EHA) system applied in rear axle steering (RAS) is used to steer the rear auxiliary wheels of large commercial vehicles and prevent oversteering. For safety reasons, commercial vehicles reduce the steering angle as the load applied to the rear wheels increases or the speed of the vehicle increases. In this study, we present a well-composed model based design (MBD) of an RAS system employing EHA used in large commercial vehicles. In order to apply loads to the rear of the vehicle to validate the legitimacy of the modeling and to replace the actual model's experiments with the simulations, the rotational speed of the motor obtained in the experiment was input to the motor of the simulation model. The organized formulas of the steering angle based on the structural form were defined to compare the steering angle against both the cylinder displacement of the actual model and the simulated model. We also applied load working on the steering wheel to check the angle difference according to the weight. The organized formulas were applied in the MBD system to compare the results of simulation modeling with the actual experimental values to confirm the modeling's validity. [ABSTRACT FROM AUTHOR]

In this paper, the parameter optimization and error analysis of the rack and pinion steering mechanism are carried out on the basis of considering the influence of kingpin parameters. The steering characteristic equation describing the motion of the steering mechanism is calculated by analyzing the spatial geometrical relationship between the wheel and kingpin and unifying the projection relation between the kingpin and the equivalent steering trapezoid. The ideal Ackermann equation is modified by the Ackermann rate and the kingpin parameters. The modified Ackermann equation is used as the objective function. The segmented fitness function and the evaluation function with weighted factors are designed. A genetic algorithm containing the three functions is used to optimize the parameters of the steering characteristic equation. The error analysis of the numerical example shows that the accuracy of steering trapezoid structure parameters, steering characteristic equation, and Ackermann equation is improved compared with that before optimization. [ABSTRACT FROM AUTHOR]

A fully autonomous vehicle must ensure not only fully autonomous driving but also the safety and comfort of its passengers. However, the self-driving technology that is currently completed focuses only on perfect driving and does not guarantee the safety and comfort of passengers. This paper proposes a braking-pressure and driving-direction determination system (BDDS), which computes the brake pressure and steering angle optimized for passenger safety by utilizing more diverse information than existing autonomous vehicles. The BDDS proposed in this paper consists of two modules. The road roughness classification module (RRCM) classifies the roughness of the road by using the pressure data applied to the suspension and the K-NN algorithm and computes the optimal brake pressure. The passenger recognition and sharing module (PRSM) identifies the current occupant status of the vehicle by using a body pressure sensor and CNN, shares the information with surrounding vehicles, and computes the optimal steering angle using passenger information and road information. As a result of the simulations described in this paper, the parameters of AI models were optimized. In addition, the RRCS was about 7% more accurate than the K-means clustering algorithm, and PRS was about 9% more accurate than the existing seat recognition system. [ABSTRACT FROM AUTHOR]

• In addressing the issue of diminishing peak side lobe levels (PSLL) in non-uniform optical phased array (OPA) as steering angle increases, we introduce a dual-adaption genetic algorithm (DAGA) based on chaos sequence, which optimizes the antenna array layout. This is the first time that an improved chaos sequence is introduced into the genetic algorithm and a dual adaptive degree function is performed to optimize a non-uniform OPA;. • Our findings indicate that, in comparison to traditional genetic algorithm, the PSLL at a steering angle of 80° improved by 2.18, 2.81, and 3.59 dB for the 64-, 128-, and 256-channel respectively;. • The PSLL for 256-channels can be optimized up to an impressive −18.46 dB using this method. Compared with related literature, according to the existing data, under the same number of channels, the steering angle optimized by this algorithm has the lowest PSLL. To address the issue of diminishing peak side lobe levels (PSLL) in non-uniform optical phased array (OPA) as steering angle increases, we introduce a dual-adaption genetic algorithm (DAGA) based on chaos sequence to optimize the antenna array layout. By leveraging chaos mapping traversal and randomness, this approach enhances the diversity of the initial antenna layout, thereby improving the algorithm's capacity to escape local optima. Concurrently, we also reduce background noise during the array steering process to enhance PSLL at large steering angle. In this study, we optimize non-uniform antenna arrays with 64-, 128-, and 256-channels. The results demonstrate that at a steering angle of 80°, the PSLL is improved by 2.18, 2.81, and 3.59 dB, respectively, compared to the conventional genetic algorithm (GA). Notably, for a 256-channel array, the PSLL can be optimized to an impressive -18.46 dB using this method. To our knowledge, this represents the lowest PSLL achieved for an equivalent number of channels. Our research offers valuable insights for the practical implementation of OPAs as transmitters in chip-scale frequency modulated continuous wave (FMCW) light detection and ranging (LiDAR) systems. [ABSTRACT FROM AUTHOR]

Mobil listrik enggang evo3 merupakan salah satu mobil listrik Institut Teknologi Kalimantan (ITK) yang akan diikutkan pada kompetisi mobil listrik di Indonesia. Pengembangan mobil listrik terus dilakukan untuk memaksimalkan performa mobil. Permasalahan yang dihadapi oleh mobil listrik enggang evo3 adalah pada saat bergerak dengan dengan lintasan lurus, berbelok, atau slalom, mobil belum bisa mengikuti lintasan dengan baik. Sudut steer yang digunakan pada mobil enggang evo3 masih belum tepat untuk kondisi lintasan pada perlombaan. Sehingga,dilakukan penelitian tentang studi pengaruh sudut steer terhadap kecepatan lurus dan berbelok pada mobil listrik enggang evo 3. Tujuan dari penelitian ini adakah untuk mengetahui sudut belok mana yang terbaik pada kecepatan dan waktu tempuh mobil enggang evo 3 pada saat berbelok dengan radius belok lintasan yang sudah diketahui pada lintasan perlombaan. Variasi yang digunakan pada penelitian adalah sudut steer 200, 250, 300. Hasil dari penelitian menunjukan bahwa sudut steer terbaik untuk kecepatan dan waktu tempuh adalah sudut steer 20o dengan waktu tempuh 9,70 s dan kecepatan 29,66 km/h. karena semakin kecil sudut steer maka semakin besar kecepatan yang di hasilkan dan semakin besar kecepatan yang dihasilkan maka semakin singkat waktu tempuh yang diperlukan.

Control of pilotless electric vehicles indoors was studied. The issue of ensuring precise correspondence of control signals to the actual motion of transport vehicles in a room was examined. During the course of analyzing the dynamic model of the electric vehicle, it was discovered that the primary reasons for the occurrence of errors consists in the indirect nature of measuring the velocity of this vehicle by means of odometers. In order to solve this problem, it is proposed to use microwave Doppler sensors of the linear velocity of the wheels, observations of which do not depend on sliding, the mass of the electric vehicle, the voltage in the buses, and other parameters. Formulas were developed that make it possible, using the Doppler frequencies of sensors, to determine the current velocity and heading angle of electric vehicle control with the use of the Ackermann condition. Based on the specified formulas, a stable system for traffic control of pilotless transport electric vehicles on a specified route was developed. The proposed system can be used in the robotic transport systems. [ABSTRACT FROM AUTHOR]

Unintentional lane departure accidents are one of the biggest reasons for the causalities that occur due to human errors. By incorporating lane-keeping features in vehicles, many accidents can be avoided. The lane-keeping system operates by auto-steering the vehicle in order to keep it within the desired lane, despite of changes in road conditions and other interferences. Accurate steering angle prediction is crucial to keep the vehicle within the road boundaries, which is a challenging task. The main difficulty in this regard is to identify the drivable road area on heterogeneous road types varying in color, texture, illumination conditions, and lane marking types. This strenuous problem can be addressed by two approaches, namely, ‘computer-vision-based approach’ and ‘imitation-learning-based approach’. To the best of our knowledge, at present, there is no such detailed review study covering both the approaches and their related optimization techniques. This comprehensive review attempts to provide a clear picture of both approaches of steering angle prediction in the form of step by step procedures. The taxonomy of steering angle prediction has been presented in the paper for a better comprehension of the problem. We have also discussed open research problems at the end of the paper to help the researchers of this area to discover new research horizons.

This study presents smooth and fast feasible trajectory generation for autonomous driving vehicles subject to the vehicle physical constraints on the vehicle power, speed, acceleration as well as the hard limitations of the vehicle steering angle and the steering angular speed. This is due to the fact the vehicle speed and the vehicle steering angle are always in a strict relationship for safety purposes, depending on the real vehicle driving constraints, the environmental conditions, and the surrounding obstacles. Three different methods of the position quintic polynomial, speed quartic polynomial, and symmetric polynomial function for generating the vehicle trajectories are presented and illustrated with simulations. The optimal trajectory is selected according to three criteria: Smoother curve, smaller tracking error, and shorter distance. The outcomes of this paper can be used for generating online trajectories for autonomous driving vehicles and auto-parking systems. [ABSTRACT FROM AUTHOR]

This paper proposes an efficient path planning technique for the autonomous collision-free navigation of wheeled mobile robots with simple hardware based on an adaptive neuro-fuzzy inference system (ANFIS). The distance between the robot and obstacles is measured using three ultrasonic sensors that are installed on the left, front, and right of the robot. These distances from the sensors form the inputs to the ANFIS-utility function block that calculates an obstacle avoidance steering angle for the robot. The obstacle avoidance behavior of the robot is modeled under six scenarios of facing an obstacle. The instantaneous position of the robot and the target are available from Global Positioning System (GPS) modules. A simulation mobile robot in V-REP has been integrated into the ANFIS controller coded in MATLAB. The simulation results show that the proposed ANFIS-utility function-based path planning technique surpasses some of the related algorithms in terms of finding near-optimal paths.

A fully autonomous vehicle must ensure not only fully autonomous driving but also the safety and comfort of its passengers. However, the self-driving technology that is currently completed focuses only on perfect driving and does not guarantee the safety and comfort of passengers. This paper proposes a braking-pressure and driving-direction determination system (BDDS), which computes the brake pressure and steering angle optimized for passenger safety by utilizing more diverse information than existing autonomous vehicles. The BDDS proposed in this paper consists of two modules. The road roughness classification module (RRCM) classifies the roughness of the road by using the pressure data applied to the suspension and the K-NN algorithm and computes the optimal brake pressure. The passenger recognition and sharing module (PRSM) identifies the current occupant status of the vehicle by using a body pressure sensor and CNN, shares the information with surrounding vehicles, and computes the optimal steering angle using passenger information and road information. As a result of the simulations described in this paper, the parameters of AI models were optimized. In addition, the RRCS was about 7% more accurate than the K-means clustering algorithm, and PRS was about 9% more accurate than the existing seat recognition system.

In this paper, a linear two degrees of freedom linear bicycle model is proposed to investigate the vehicle handling criterion. The study is based on simulation developed using MATLAB / Simulink to predict the vehicle dynamic stability. Steering angle is given as an input to the mathematical model for various vehicular manoeuvres. This model is validated using a step input which is adjusted to give 0.3g lateral acceleration. The system model is simulated under a typical front wheel steering to examine the highway vehicle prediction output within its manoeuvre. This input is also adjusted to keep lateral acceleration value in steady state region. It is found that changing the vehicle center of gravity (CG) position, vehicle mass, tire cornering stiffness and vehicle speed all have a significant influence on the vehicle dynamic stability. [ABSTRACT FROM AUTHOR]

Vehicle sideslip angle is a major indicator of dynamics stability for ground vehicles; but it is immeasurable with commercially‐available sensors. Sideslip angle estimation has been the focus of intensive research in past decades, resulting in a rich library of related literature. This study presents a comprehensive evaluation of state‐of‐the‐art sideslip angle estimation methods, with the primary goal of quantitatively revealing their strengths and limitations. These include kinematics‐, dynamics‐ and neural network‐based estimators. A hardware‐in‐loop system is purposely established to examine their performance under four typical manoeuvres. The results show that the dynamics‐based estimators are suitable at low vehicle velocities when tires operate in the linear region. In contrast, the kinematics‐based methods yield superior estimation performance at high vehicle velocities, and the inclusion of the dual GPS receivers is beneficial even when there is large disturbance to the steering angle. Of utmost importance, it is experimentally manifested that the neural network‐based estimator can perform well in all manoeuvres once the training datasets are properly selected. [ABSTRACT FROM AUTHOR]

In this paper, a linear two degrees of freedom linear bicycle model is proposed to investigate the vehicle handling criterion. The study is based on simulation developed using MATLAB / Simulink to predict the vehicle dynamic stability. Steering angle is given as an input to the mathematical model for various vehicular manoeuvres. This model is validated using a step input which is adjusted to give 0.3g lateral acceleration. The system model is simulated under a typical front wheel steering to examine the highway vehicle prediction output within its manoeuvre. This input is also adjusted to keep lateral acceleration value in steady state region. It is found that changing the vehicle center of gravity (CG) position, vehicle mass, tire cornering stiffness and vehicle speed all have a significant influence on the vehicle dynamic stability. [ABSTRACT FROM AUTHOR]

In this paper, prior to the commercialization of a developed active steering bogie, we want to analyze steering performance experimentally according to steering angle level with the aim of obtaining steering performance data to derive practical design specifications for a steering system. In other words, the maximum steering performance can be obtained by controlling the steering angle at the 100% level of the target steering angle, but it is necessary to establish the practical control range in consideration of the steering system cost increase, size increase, and consumer steering performance requirements and commercialize. The steering control test using the active steering bogie was conducted in the section of the steep curve with a radius of curvature of R300, and steering performance such as bogie angle, wheel lateral force, and derailment coefficient were analyzed according to the steering angle level. As the steering angle level increased, the bogie indicated that it was aligned with the radial steering position, and steering performance such as wheel lateral force and derailment coefficient was improved. The steering control at 100% level of the target steering angle can achieve the highest performance of 83.6% reduction in wheel lateral force, but it can be reduced to about one-half of the conventional bogie at 25% level control and about one-third at 50% level. Considering cost rise by adopting the active steering system, this result can be used as a very important design indicator to compromise steering performance and cost rise issues in the design stage of the steering system from a viewpoint of commercialization. Therefore, it is expected that the results of the steering performance experiment according to the steering angle level in this paper will be used as very useful data for commercialization. [ABSTRACT FROM AUTHOR]

The impact of the kinematic parameters of bounce and pitch motions of wheeled vehicles’ (WVs’) sprung mass (SM) with non-linear power characteristics of the cushion system on vehicles handling is studied. The dependence of the critical value of the dynamic steering angle of directive wheels on the amplitude of bounce and pitch motions and the kinematic parameters of motion is developed. It is proven that the limit value of the dynamic steering angle of directive wheels is reduced during acceleration, and vice versa (it increases during braking, while the bounce and pitch motions are significantly reduced).

Detection of drivers' states is the essential technology not only to prevent car accidents related with their state but to develop self-driving car. Detecting technology generally uses two types of methods; physiological measures and vehicle-based measures. Vehicle-based measures have advantages compared to physiological method such as non-additional device, unsophisticated process and less computational power. For these reasons, vehicle-based measures are used for this study to build the detection system about 3 states; normal, drowsy and drunk driving. In order to achieve this purpose, three types of algorithm models are suggested using vehicle simulator experiments with twelve participants on three states; normal, drowsy and drunk. By analyzing the accuracy of each input packet data combination, the feature values, the configuration of the input data calculated through the vehicle driving data is used to derive the influential factors for predicting the driver state. The results of the models indicate high accuracy and give the possibility to be applied on detecting 3 states in real driving vehicles with the system using combination of developed models. [ABSTRACT FROM AUTHOR]

Precision agriculture, such as precise seeding or ploughing, is guaranteed by vehicle navigation accuracy. A steering angle plays an essential and fundamental role in the function of autonomous vehicle and the path planning because it controls the vehicle direction. However, the steering angle cannot be calibrated directly from Global Navigation Satellite System (GNSS) due to the limitation of GNSS parameters. In addition, the accuracy of steering angle obtained from the analysis of Global Position System (GPS) information cannot satisfy the accuracy requirements in engineering applications. In this paper, a novel approach to improve accuracy of steering angle is proposed. Low-cost Micro-electromechanical System (MEMS) and GPS are combined on an agriculture vehicle so that the steering angle can be calculated precisely. Unscented Kalman Filters (UKF) are used in simulations to estimate the steering angle. The simulation and experiment results show that the proposed method can achieve increased accuracy of estimation of steering angle than that based merely on GPS information. Because the cost of a MEMS gyroscope is very low, the proposed method is a practical way to achieve precision agriculture with high accuracy. [ABSTRACT FROM AUTHOR]

Driver drowsiness is a factor in at least 20% of serious motor vehicle accidents. Although research has shown that Whole-Body Vibration (WBV) can induce drowsiness in drivers, it is unknown whether particular frequencies are more problematic. The present study systematically investigated the influence of WBV frequency on driver drowsiness. Fifteen participants each undertook six 1-h sessions of simulated driving while being subjected to WBV of either 0 Hz (no vibration), 1–4 Hz, 4–8 Hz, 8–16 Hz, 16–32 Hz or 32–64 Hz. Subjective sleepiness, as measured by the Karolinska Sleepiness Scale (KSS), confirmed that drivers felt drowsier when exposed to the two lowest frequency ranges (1–4 Hz and 4–8 Hz). Reaction time, which measures attention and alertness, was significantly impaired by the two lowest frequency ranges. Objective driving performance measures (Standard Deviation of Lane Position (SDLP), Standard Deviation of (SD) Steering Angle, Time in Unsafe Zone) also showed significant degradation due to exposure to the two lowest frequency ranges. Exposure to 1–4 Hz or 4–8 Hz vibration caused attention to become significantly impaired within 15–20 min and driving performance to be significantly impaired by 30–35 min. The other frequency ranges had little or no effect. These findings point to a need to develop equivalent vibration-induced drowsiness contours that can be adopted as transportation safety standards. • The effects of 60 min of whole-body vibration on reaction time, and driving performance were systematically examined. • Reaction times were significantly slowed after 15–20 min of exposure to 1–4 Hz and 4–8 Hz. • Driving metrics were significantly degraded after 30–35 min exposure to 1–4 Hz and 4–8 Hz. • Higher frequencies (8–64 Hz) had a minimal effect on drowsiness and driving performance. [ABSTRACT FROM AUTHOR]

This study presents smooth and fast feasible trajectory generation for autonomous driving vehicles subject to the vehicle physical constraints on the vehicle power, speed, acceleration as well as the hard limitations of the vehicle steering angle and the steering angular speed. This is due to the fact the vehicle speed and the vehicle steering angle are always in a strict relationship for safety purposes, depending on the real vehicle driving constraints, the environmental conditions, and the surrounding obstacles. Three different methods of the position quintic polynomial, speed quartic polynomial, and symmetric polynomial function for generating the vehicle trajectories are presented and illustrated with simulations. The optimal trajectory is selected according to three criteria: Smoother curve, smaller tracking error, and shorter distance. The outcomes of this paper can be used for generating online trajectories for autonomous driving vehicles and auto-parking systems.