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24 pages, 1781 KiB

Open AccessEditor’s ChoiceArticle

Learning-Based MPC Leveraging SINDy for Vehicle Dynamics Estimation

by Francesco Paparazzo, Andrea Castoldi, Mohammed Irshadh Ismaaeel Sathyamangalam Imran, Stefano Arrigoni and Francesco Braghin

Electronics 2025, 14(10), 1935; https://doi.org/10.3390/electronics14101935 - 9 May 2025

Viewed by 262

Abstract

Self-driving technology aims to minimize human error and improve safety, efficiency, and mobility through advanced autonomous driving algorithms. Among these, Model Predictive Control (MPC) is highly valued for its optimization capabilities and ability to manage constraints. However, its effectiveness depends on an accurate [...] Read more.

Self-driving technology aims to minimize human error and improve safety, efficiency, and mobility through advanced autonomous driving algorithms. Among these, Model Predictive Control (MPC) is highly valued for its optimization capabilities and ability to manage constraints. However, its effectiveness depends on an accurate system model, as modeling errors and disturbances can degrade performance, making uncertainty management crucial. Learning-based MPC addresses this challenge by adapting the predictive model to changing and unmodeled conditions. However, existing approaches often involve trade-offs: robust methods tend to be overly conservative, stochastic methods struggle with real-time feasibility, and deep learning lacks interpretability. Sparse regression techniques provide an alternative by identifying compact models that retain essential dynamics while eliminating unnecessary complexity. In this context, the Sparse Identification of Nonlinear Dynamics (SINDy) algorithm is particularly appealing, as it derives governing equations directly from data, balancing accuracy and computational efficiency. This work investigates the use of SINDy for learning and adapting vehicle dynamics models within an MPC framework. The methodology consists of three key phases. First, in offline identification, SINDy estimates the parameters of a three-degree-of-freedom single-track model using simulation data, capturing tire nonlinearities to create a fully tunable vehicle model. This is then validated in a high-fidelity CarMaker simulation to assess its accuracy in complex scenarios. Finally, in the online phase, MPC starts with an incorrect predictive model, which SINDy continuously updates in real time, improving performance by reducing lap time and ensuring a smoother trajectory. Additionally, a constrained version of SINDy is implemented to avoid obtaining physically meaningless parameters while aiming for an accurate approximation of the effects of unmodeled states. Simulation results demonstrate that the proposed framework enables an adaptive and efficient representation of vehicle dynamics, with potential applications to other control strategies and dynamical systems. Full article

(This article belongs to the Special Issue Feature Papers in Electrical and Autonomous Vehicles)

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21 pages, 2649 KiB

Open AccessEditor’s ChoiceArticle

A Novel Approach for Self-Driving Vehicle Longitudinal and Lateral Path-Following Control Using the Road Geometry Perception

by Felipe Barreno, Matilde Santos and Manuel Romana

Electronics 2025, 14(8), 1527; https://doi.org/10.3390/electronics14081527 - 10 Apr 2025

Viewed by 371

Abstract

This study proposes an advanced intelligent vehicle path-following control system using deep reinforcement learning, with a particular focus on the role of road geometry perception in motion planning and control. The system is structured around a three-degree-of-freedom (3-DOF) vehicle model, which facilitates the [...] Read more.

This study proposes an advanced intelligent vehicle path-following control system using deep reinforcement learning, with a particular focus on the role of road geometry perception in motion planning and control. The system is structured around a three-degree-of-freedom (3-DOF) vehicle model, which facilitates the extraction of critical dynamic features necessary for robust control. The longitudinal control architecture integrates a Deep Deterministic Policy Gradient (DDPG) agent to optimise longitudinal velocity and acceleration, while lateral vehicle control is handled by a Deep Q-Network (DQN). To enhance situational awareness and adaptability, the system incorporates key input variables, including ego vehicle speed, speed error, lateral deviation, lateral error, and safety distance to the preceding vehicle, all in the context of road geometry and vehicle dynamics. In addition, the influence of road curvature is embedded into the control framework through perceived acceleration (sensed by vehicle occupants), allowing for more accurate and responsive adaptation to varying road conditions. The vehicle control system is tested in a simulated environment with a lead car in front with realistic speed profiles. The system outputs continuous values for acceleration and steering angle. The results of this study suggest that the proposed intelligent control system not only improves driver assistance but also has potential applications in autonomous driving. This framework contributes to the development of more autonomous, efficient, safety-aware, and comfortable vehicle control systems. Full article

(This article belongs to the Special Issue Feature Papers in Electrical and Autonomous Vehicles)

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17 pages, 991 KiB

Open AccessArticle

Collision Probabilities Between User Equipment Using 5G NR Sidelink Time-Domain-Based Resource Allocation in C-V2X

by Mehnaz Tabassum and Aurenice Oliveira

Electronics 2025, 14(4), 751; https://doi.org/10.3390/electronics14040751 - 14 Feb 2025

Viewed by 581

Abstract

Efficient resource allocation is a critical factor in ensuring reliable and low-latency communication in the fifth-generation New Radio (5G NR) sidelink-based Cellular Vehicle to Everything (C-V2X) networks. One of the critical challenges in adopting C-V2X systems is the potential for packet collisions between [...] Read more.

Efficient resource allocation is a critical factor in ensuring reliable and low-latency communication in the fifth-generation New Radio (5G NR) sidelink-based Cellular Vehicle to Everything (C-V2X) networks. One of the critical challenges in adopting C-V2X systems is the potential for packet collisions between User Equipment (UE) when they share resources in the sidelink channel. For reliable and low-latency communication, especially in safety-critical applications, efficient resource allocation is essential. This paper explores collision-related issues that may arise in the 5G NR sidelink and the probability of collisions on resource blocks. To address these challenges, we propose an experimental time-domain resource allocation strategy leveraging dynamic reselection intervals and adaptive reservation mechanisms. Unlike existing approaches, which primarily rely on static or semi-persistent scheduling, our strategy optimizes resource allocation based on real-time variations in generation time, speed and distance between UEs. The proposed approach significantly reduces collision probabilities, enhances communication reliability and ensures efficient resource utilization, even in high-density vehicular networks. Addressing packet collisions in resource allocation becomes crucial for the viability of vehicular communication systems. The goal of this paper is to analyze the dynamics and causes of packet collisions in C-V2X scenarios using 5G NR sidelink technology and to evaluate how our time-domain optimization techniques can enhance system performance in rapidly evolving vehicular communication networks. Full article

(This article belongs to the Special Issue Feature Papers in Electrical and Autonomous Vehicles)

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23 pages, 5530 KiB

Open AccessArticle

A Novel Heuristic Algorithm Integrating Battery Digital-Twin-Based State-of-Charge Estimation for Optimized Electric Vehicle Charging

by Nandini G. Iyer, Sivakumar Ponnurangan, Nazar Ali Abdul Gafoor, Anand Rajendran, Abderezak Lashab, Diptish Saha and Josep M. Guerrero

Electronics 2024, 13(22), 4412; https://doi.org/10.3390/electronics13224412 - 11 Nov 2024

Viewed by 1374

Abstract

The increasing need for effective electric vehicle (EV) charging solutions in the context of transportation electrification has become a significant challenge. This system introduces an innovative algorithm, named Energy Distribution and Node Allocation using Evolutionary and Resourceful Optimization (ENDEAVOR), designed to elevate the [...] Read more.

The increasing need for effective electric vehicle (EV) charging solutions in the context of transportation electrification has become a significant challenge. This system introduces an innovative algorithm, named Energy Distribution and Node Allocation using Evolutionary and Resourceful Optimization (ENDEAVOR), designed to elevate the efficiency of EV charging through the integration of a battery’s digital twin. This cutting-edge algorithm offers precise estimations of EV charging time, seamlessly updating both the State of Charge (SOC) via the Unscented Kalman Filter (UKF) and the internal battery resistance using parameterization, while sending this information to the cloud. ENDEAVOR optimizes charging-node allocation and intelligently distributes energy among incoming EVs based on their specific charging requests, all within the context of renewable-energy-sourced charging stations. The incorporation of a digital twin for the battery confers several benefits, including highly accurate SOC and charging-time estimates that ultimately enhance the overall efficiency of the charging process. This algorithm further optimizes energy distribution, resulting in significantly improved charging-time predictions, reduced wait times for users, and an enhanced overall experience for the user. The day-to-day implications of these enhancements are remarkable, culminating in substantial annual energy savings of approximately 180 units. ENDEAVOR has the potential to revolutionize the landscape of EV charging. Full article

(This article belongs to the Special Issue Feature Papers in Electrical and Autonomous Vehicles)

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22 pages, 3935 KiB

Open AccessArticle

Research on Yaw Stability Control Strategy Based on Direct Slip Rate Allocation

by Jing Li, Jin Luo, Shiyao Guo and Baidong Feng

Electronics 2024, 13(1), 193; https://doi.org/10.3390/electronics13010193 - 1 Jan 2024

Cited by 4 | Viewed by 2103

Abstract

This paper deals with the integrated control of trajectory tracking and yaw stability for autonomous vehicles. Firstly, a nonlinear vehicle dynamics model is established. The MPC algorithm was used to determine the best front wheel angle. The PID control algorithm is used to [...] Read more.

This paper deals with the integrated control of trajectory tracking and yaw stability for autonomous vehicles. Firstly, a nonlinear vehicle dynamics model is established. The MPC algorithm was used to determine the best front wheel angle. The PID control algorithm is used to ensure the accuracy of longitudinal speed tracking. The sliding mode control algorithm is used to generate additional yaw moment and optimize the distribution of longitudinal tire force. In order to ensure the most effective distribution of the driving torque of the four wheels of the vehicle, the PID algorithm is used to track and manage the longitudinal slip rate of each tire on the bottom layer. Simulation tools such as MATLAB/Simulink and Carsim are used to verify the effectiveness of the multi-closed-loop integrated control technology. Compared with the general control strategy (no slip rate control), the trajectory error of this control algorithm is reduced by 55.6%, which indicates that it has advantages in obtaining a high tracking accuracy and ensuring the stability of autonomous vehicles. Full article

(This article belongs to the Special Issue Feature Papers in Electrical and Autonomous Vehicles)

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24 pages, 85251 KiB

Open AccessArticle

Multimodal Global Trajectory Planner for Autonomous Underwater Vehicles

by Rafał Kot

Electronics 2023, 12(22), 4602; https://doi.org/10.3390/electronics12224602 - 10 Nov 2023

Cited by 2 | Viewed by 1806

Abstract

The underwater environment introduces many limitations that must be faced when designing an autonomous underwater vehicle (AUV). One of the most important issues is developing an effective vehicle movement control and mission planning system. This article presents a global trajectory planning system based [...] Read more.

The underwater environment introduces many limitations that must be faced when designing an autonomous underwater vehicle (AUV). One of the most important issues is developing an effective vehicle movement control and mission planning system. This article presents a global trajectory planning system based on a multimodal approach. The trajectory of the vehicle’s movement has been divided into segments between introduced waypoints and calculated in parallel by advanced path planning methods: modified A* method, artificial potential field (APF), genetic algorithm (GA), particle swarm optimisation (PSO), and rapidly-exploring random tree (RRT). The shortest paths in each planned segment are selected and combined to give the resulting trajectory. A comparison of the results obtained by the proposed approach with the path calculated by each method individually confirms the increase in the system’s effectiveness by ensuring a shorter trajectory and improving the system’s reliability. Expressing the final trajectory in the form of geographical coordinates with a specific arrival time allows the implementation of calculation results in mission planning for autonomous underwater vehicles used commercially and in the military, as well as for autonomous surface vehicles (ASVs) equipped with trajectory tracking control systems. Full article

(This article belongs to the Special Issue Feature Papers in Electrical and Autonomous Vehicles)

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11 pages, 4144 KiB

Open AccessArticle

An Anti-Different Frequency Interference Algorithm for Environment-Aware Millimeter Wave Radar

by Jinzhou Dai, Lei Du, Shuo Sha, Chao Han and Yao Yao

Electronics 2023, 12(18), 3797; https://doi.org/10.3390/electronics12183797 - 8 Sep 2023

Viewed by 1345

Abstract

An environment-aware millimeter wave radar (EMWR) is one of the most important sensors in an autonomous driving system. With the increasing penetration of an EMWR on an autonomous vehicle, the possibility of radar interference increases accordingly. Interference may seriously affect the detection performance [...] Read more.

An environment-aware millimeter wave radar (EMWR) is one of the most important sensors in an autonomous driving system. With the increasing penetration of an EMWR on an autonomous vehicle, the possibility of radar interference increases accordingly. Interference may seriously affect the detection performance of an EMWR. When the transmitted signals from other EMWRs are received by the EMWR on an autonomous vehicle, the accuracy of target detection will be affected, which may affect the environment-aware performance. Therefore, more and more attention is paid to how to restrain the interference between EMWR signals. Radar interference can be divided into two types: same-frequency interference (SFI) and different-frequency interference (DFI). In this paper, an anti-DFI algorithm of an EMWR is proposed. Firstly, the causes and signal characteristics of DFI are analyzed. Secondly, the signal amplitude is obtained via Hilbert transform to locate the interference according to the signal characteristics. Then, the Lagrange interpolation based on empirical-mode decomposition (EMD) is used to reconstruct the interference signal to reduce the influence of DFI. Finally, the feasibility of the proposed algorithm is verified by the simulation results. The simulation results validate that the interference signal after EMD filtering and Lagrange interpolation can repair the interference region and achieve the purpose of anti-DFI. Full article

(This article belongs to the Special Issue Feature Papers in Electrical and Autonomous Vehicles)

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20 pages, 2872 KiB

Open AccessArticle

A Bi-Objective Simulation Facility for Speed and Range Calibration of 24 GHz and 77 GHz Automotive Millimeter-Wave Radars for Environmental Perception

by Tianqi Xu, Danyang Yu and Lei Du

Electronics 2023, 12(13), 2947; https://doi.org/10.3390/electronics12132947 - 4 Jul 2023

Cited by 3 | Viewed by 1700

Abstract

Automotive millimeter-wave radars are widely used to measure kinematic parameters of surrounding objects in the environmental perception of automated vehicles and typically operate in frequency bands of around 24 GHz and 77 GHz. The 24 GHz and 77 GHz automotive millimeter-wave radars are [...] Read more.

Automotive millimeter-wave radars are widely used to measure kinematic parameters of surrounding objects in the environmental perception of automated vehicles and typically operate in frequency bands of around 24 GHz and 77 GHz. The 24 GHz and 77 GHz automotive millimeter-wave radars are similar in their speed and range measurement principles; however, they are distinct in their accuracies when measuring the above kinematic parameters because of the different carrier frequencies and bandwidths. In order to evaluate the kinematic parameter measurement performances of automotive millimeter-wave radars for environmental perception, this paper proposes a calibration method and establishes a bi-objective simulation facility for the speed and range calibration of 24 GHz and 77 GHz automotive millimeter-wave radars. The feasibility of the proposed calibration method and the accuracy of the established bi-objective simulation facility are verified by the results obtained via experiments conducted on two 24 GHz and 77 GHz millimeter-wave radar samples. According to the speed and range calibration results of the two millimeter-wave radar samples, the 77 GHz millimeter-wave radar sample possesses superior speed measurement accuracy compared with the 24 GHz sample. Additionally, the range measurement errors of the 77 GHz millimeter-wave radar sample are much smaller than those of the 24 GHz sample. This indicates that the 77 GHz millimeter-wave radar sample is more accurate and reliable than the 24 GHz sample. Finally, the 77 GHz millimeter-wave radar sample was tested for its speed and range resolving ability. The results confirm that the bi-objective simulation facility can satisfy the calibration requirements of speed resolution up to 0.01 m/s and range resolution up to 0.1 m. The proposed calibration method and the established simulation facility can easily satisfy the speed, range and resolution calibration requirements of 24 GHz and 77 GHz millimeter-wave radars with high precision and wide range, and can achieve more economical, comprehensive and traceable performance testing on both 24 GHz and 77 GHz automotive millimeter-wave radars for environmental perception. Full article

(This article belongs to the Special Issue Feature Papers in Electrical and Autonomous Vehicles)

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15 pages, 8952 KiB

Open AccessArticle

State Parameter Estimation of Intelligent Vehicles Based on an Adaptive Unscented Kalman Filter

by Yu Wang, Yushan Li and Ziliang Zhao

Electronics 2023, 12(6), 1500; https://doi.org/10.3390/electronics12061500 - 22 Mar 2023

Cited by 12 | Viewed by 2648

Abstract

The premise of vehicle intelligent decision making is to obtain vehicle motion state parameters accurately and in real-time. Several state parameters cannot be measured directly by vehicle sensors, so estimation algorithms based on filtering are effective solutions. The most representative algorithm is the [...] Read more.

The premise of vehicle intelligent decision making is to obtain vehicle motion state parameters accurately and in real-time. Several state parameters cannot be measured directly by vehicle sensors, so estimation algorithms based on filtering are effective solutions. The most representative algorithm is the Kalman filter, especially the standard unscented Kalman filter (UKF) that has been widely used in vehicle state estimation because of its superiority in dealing with nonlinear filtering problems. However, although the UKF assumes that the noise statistics of the system are known, due to the complex and changeable operating conditions, sensor aging and other factors, these noises vary. In order to realize high-precision vehicle state estimation, a noise-adaptive UKF algorithm is proposed in this article. The maximum a posteriori (MAP) algorithm is used to dynamically update the noise of the vehicle system, and it is embedded into the update step of the UKF to form an adaptive unscented Kalman filter (AUKF). The system will dynamically update the noise when noise statistics are unknown and prevent filter divergence by adjusting the mean and covariance of the estimated noise to improve accuracy. On this basis, the proposed method is verified by the joint simulation of CarSim and Matlab/Simulink, confirming that the AUKF performs better than the standard UKF in estimation accuracy and stability under different degrees of noise disturbance, and the estimation accuracy for the yaw rate, side slip angle and longitudinal velocity is improved by 20.08%, 40.98% and 89.91%, respectively. Full article

(This article belongs to the Special Issue Feature Papers in Electrical and Autonomous Vehicles)

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14 pages, 3919 KiB

Open AccessArticle

Thermal Analysis of Water-Cooling Permanent Magnet Synchronous Machine for Port Traction Electric Vehicle

by Yongming Tang, Shouguang Sun, Wenfei Yu and Wei Hua

Electronics 2023, 12(3), 734; https://doi.org/10.3390/electronics12030734 - 1 Feb 2023

Cited by 5 | Viewed by 3958

Abstract

To further increase the torque/power density of a permanent magnet synchronous machine (PMSM) employed for a port traction electric vehicle, improving the thermal dissipation capacity of the cooling system used in the PMSM has become more and more important. This paper focuses on [...] Read more.

To further increase the torque/power density of a permanent magnet synchronous machine (PMSM) employed for a port traction electric vehicle, improving the thermal dissipation capacity of the cooling system used in the PMSM has become more and more important. This paper focuses on the thermal analysis of a water-cooling 200 kW PMSM for a port traction electric vehicle. First, the size parameters of the machine and the thermal property parameters of the materials used for each component are given. Based on the heat transfer theory, a fast evaluation method for a transient temperature rise in the water-cooling machine under multiple operating conditions is proposed. A lumped parameter thermal network (LPTN) model is established, and the temperature distributions of the machine at different operating conditions are analyzed. Second, under the same conditions, based on computational fluid dynamics (CFD), a three-dimensional (3D) CFD model is constructed. The influence of different cooling structures on temperature distribution is studied. The validity of the proposed fast evaluation method for a transient temperature rise in water-cooling machines under multiple operating conditions is verified. Finally, the results of the CFD and LPTN calculation are verified by experiments; the maximum temperature deviation of the rated speed/rated power operating condition is 8.5%. This paper provides a reference for the design and analysis of port traction electric vehicle machines. Full article

(This article belongs to the Special Issue Feature Papers in Electrical and Autonomous Vehicles)

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17 pages, 4534 KiB

Open AccessArticle

Data-Driven Fault Early Warning Model of Automobile Engines Based on Soft Classification

by Xiufeng Li, Ning Wang, Yelin Lyu, Yan Duan and Jiaqi Zhao

Electronics 2023, 12(3), 511; https://doi.org/10.3390/electronics12030511 - 18 Jan 2023

Cited by 10 | Viewed by 2578

Abstract

Since automobile engine fault is the main factor leading to a vehicle breaking down, engine fault diagnosis has captured a lot of attention. Fault diagnosis identifies fault types to facilitate maintenance. However, the method of the warning before the fault occurs is more [...] Read more.

Since automobile engine fault is the main factor leading to a vehicle breaking down, engine fault diagnosis has captured a lot of attention. Fault diagnosis identifies fault types to facilitate maintenance. However, the method of the warning before the fault occurs is more attractive to users and is more challenging. Therefore, this study would like to explore the feasibility of implementing automobile engine fault early warning based on the fault diagnosis model. First, the theoretical method of a fault domain is established, and the state of the engine is regarded as a point in n-dimensional space. The normal or fault of the engine will correspond to different state domains in this space. Second, to diagnose multiple fault types at the same time, an ensemble model based on multiple machine learning methods is established. The probability outputs by the ensemble model measure the distance between the point and each fault domain in the space. Finally, considering the temporal factor, an early warning threshold is established based on the probability, and a fault warning model is established by using the dual probability structure. Comparative experiments show that the proposed method can greatly reduce the calculation time based on ensuring the accuracy of early warning and is suitable for real-time early warning of multiple faults. Full article

(This article belongs to the Special Issue Feature Papers in Electrical and Autonomous Vehicles)

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17 pages, 7462 KiB

Open AccessArticle

Electric Vehicle Powertrains with Modular Battery Banks Tied to Multilevel NPC Inverters

by Sergio Busquets-Monge, Salvador Alepuz, Gabriel García-Rojas and Josep Bordonau

Electronics 2023, 12(2), 266; https://doi.org/10.3390/electronics12020266 - 4 Jan 2023

Cited by 6 | Viewed by 2945

Abstract

Nowadays, the internal combustion engine in vehicles is being replaced by electric motors, giving way to the electric vehicle, which results in reduced environmental impact, higher efficiency and lower emission of greenhouse gases. The powertrain of an electric vehicle is its most prominent [...] Read more.

Nowadays, the internal combustion engine in vehicles is being replaced by electric motors, giving way to the electric vehicle, which results in reduced environmental impact, higher efficiency and lower emission of greenhouse gases. The powertrain of an electric vehicle is its most prominent subsystem, with the batteries and traction inverter being key components. Thus, due to their relevance, advances in the design of both components are of paramount importance. In this paper, the potential benefits achieved through a powertrain design approach based on combining a modular battery bank with multilevel NPC traction inverter topologies were analyzed, in comparison to a conventional two-level powertrain design. Several aspects were analyzed: modularity, complexity, battery-pack state-of-charge balancing, inverter loss, motor ac voltage harmonic distortion, motor common-mode voltage and reliability. Particularly, from the comparison study developed under the selected design scenario, the proposed design approach, based on modular battery packs and multilevel technology, shows a potential reduction of up to 55% in inverter losses, up to 65% in motor ac-voltage total harmonic distortion, and up to 75% in rms common-mode voltage. Full article

(This article belongs to the Special Issue Feature Papers in Electrical and Autonomous Vehicles)

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13 pages, 3104 KiB

Open AccessArticle

Research on a Random Route-Planning Method Based on the Fusion of the A* Algorithm and Dynamic Window Method

by Yicheng Sun, Xianliang Zhao and Yazhou Yu

Electronics 2022, 11(17), 2683; https://doi.org/10.3390/electronics11172683 - 26 Aug 2022

Cited by 21 | Viewed by 2744

Abstract

Path planning is a hot topic at present. Considering the global and local path planning of mobile robot is one of the challenging research topics. The objective of this paper is to create a rasterized environment that optimizes the planning of multiple paths [...] Read more.

Path planning is a hot topic at present. Considering the global and local path planning of mobile robot is one of the challenging research topics. The objective of this paper is to create a rasterized environment that optimizes the planning of multiple paths and solves barrier avoidance issues. Combining the A* algorithm with the dynamic window method, a robo-assisted random barrier avoidance method is used to resolve the issues caused by collisions and path failures. Improving the A* algorithm requires analyzing and optimizing its evaluation function to increase search efficiency. The redundant point removal strategy is then presented. The dynamic window method is utilized for local planning between each pair of adjacent nodes. This method guarantees that random obstacles are avoided in real-time based on the globally optimal path. The experiment demonstrates that the enhanced A* algorithm reduces the average path length and computation time when compared to the traditional A* algorithm. After fusing the dynamic window method, the local path is corrected using the global path, and the resolution for random barrier avoidance is visualized. Full article

(This article belongs to the Special Issue Feature Papers in Electrical and Autonomous Vehicles)

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Review

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17 pages, 22335 KiB

Open AccessReview

A Synthesis of Algorithms Determining a Safe Trajectory in a Group of Autonomous Vehicles Using a Sequential Game and Neural Network

by Józef Lisowski

Electronics 2023, 12(5), 1236; https://doi.org/10.3390/electronics12051236 - 4 Mar 2023

Cited by 5 | Viewed by 1916

Abstract

This paper presents a solution to the problem of providing an autonomous vehicle with a safe control task when moving around many other autonomous vehicles. This is achieved by developing an appropriate computer control algorithm that takes into account the possible risk of [...] Read more.

This paper presents a solution to the problem of providing an autonomous vehicle with a safe control task when moving around many other autonomous vehicles. This is achieved by developing an appropriate computer control algorithm that takes into account the possible risk of a collision resulting from both the impact of environmental disturbances and the imperfection of the rules of maneuvering in situations where many vehicles pass each other, giving the control process a decisive character. For this purpose, three types of algorithms were synthesized: kinematic and dynamic optimization with neural domains, as well as sequential game control of an autonomous vehicle. The control algorithms determine a safe trajectory, which is implemented by the actuators of the autonomous vehicle. Computer simulations of the control algorithms in the Matlab/Simulink software allow for their comparative analysis in terms of meeting the criteria for the optimality and safety of an autonomous vehicle when passing a larger number of other autonomous vehicles. For this purpose, scenarios of multidirectional and one-way traffic of autonomous vehicles were used. Full article

(This article belongs to the Special Issue Feature Papers in Electrical and Autonomous Vehicles)

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