Vehicle Autonomy Research Articles

Increasing Electric Vehicle Autonomy Using a Photovoltaic System Controlled by Particle Swarm Optimization

A photovoltaic-powered electric vehicle is a complex system that necessitates the use of a high-performance control algorithm. This paper aims to boost the performance of a photovoltaic system by employing a suitable algorithm to control the power interface. The main goal is to find an effective and optimal control law that will enable the photovoltaic generator (GPV) to generate the maximum amount of power possible. The main facts dealt with in this article are the mathematical simulation of the photovoltaic system, its function, and its characteristics, considering the synthesis of the step-up converter and the analysis of the maximum power point tracking algorithm. This study examines and compares two control techniques for extracting full power from the solar energy system. These two techniques are the classical “perturbation and observation” (P&O) method and the intelligent solution “particle swarm optimization (PSO) method.” The PSO solution is tested for two versions: the online PSO version and the table PSO version. The Simulink/MATLAB tool is used for simulation and comparative experiments based on the performance metrics provided. The study revealed that smart technology delivers improved efficiency than the classic edition.

Análisis de la viabilidad para la implementación de vehículo eléctrico que preste servicio de taxi en la ciudad de Cuenca

El transporte es un elemento esencial para el crecimiento socioeconómico, sin embargo, su desarrollo genera una serie de efectos tales como la congestión, el ruido, la emisión de sustancias nocivas al medio ambiente, el agotamiento de los recursos no renovables. Uno de los factores que inciden en la reducción de la contaminación al medioambiente es la sustitución de los vehículos de combustible convencional por otros tipos de sistemas de propulsión, incluidos los motores eléctricos. La introducción de vehículos eléctricos puede convertirse en el elemento clave en el desarrollo de la estrategia de transporte sostenible en las zonas urbanas, la demanda de estos vehículos debe ser impulsada por los propietarios de la flota de vehículos, especialmente por las empresas. Los propietarios de taxis convencionales en la ciudad de Cuenca que deben renovar sus unidades, según la AFUT- Asociación Frente Unido de Taxistas-, son escépticos sobre la renovación con unidades eléctricas que proponen las autoridades locales, debido principalmente a la incertidumbre sobre la fiabilidad y rentabilidad que ofrece este tipo de vehículo, las cuales deben ser por lo menos igual a las de un vehículo con motor de combustión interna. En esta investigación se aplicó una metodología de investigación experimental mediante el uso de un software de computadora EMOLAB y un vehículo eléctrico Kia Soul EV para obtener una base de datos con información que describe el comportamiento del vehículo eléctrico. Aplicando diseños de experimentos factorial (DOE) se utilizó las variables que tienen más influencia sobre la autonomía y consumo energético del vehículo eléctrico para su evaluación.

Az önvezető autózás kihívásai és biztonsági kérdései a digitális államban

Összefoglalás. A XXI. század első felében a korábban sok évtizeden keresztül lassan változó közlekedés gyorsított ütemben alakul át. Ez alatt a pár év alatt több változás következik be, több kihívást kell leküzdeni, mint a korábbi időszakban. Az elektromos hajtás térnyerése, új járműhasználati módok mellett a járművek autonomizálódása és összekapcsolódása jelenti az új irányokat, amelyek kihívás elé állítják nemcsak az autóipart, hanem a járművek használóit és a szabályzókat, az államot is. Kutatásainkban az önvezető autózás jelentette kihívásokat emeljük ki a többi, röviden bemutatott trend közül, majd pedig vizsgáljuk, milyen kihívásokat támaszt a digitalizálódó állam felé az önvezetés felé elmozduló járműves technológia. Summary. In the first half of the 21st century, transportation that has been slowly changing over many decades has been transforming at an accelerated rate. Over the course of these few years, there will be more changes and more challenges to overcome. For a century it was unquestionable that a vehicle is driven by a driver and its energy comes via diesel or petrol from crude oil. Today vehicles’ autonomy in driving is increasing, and instead of crude oil based fuels first biocomponents and gaseous fuels appeared, and now electricity knocks at the door. The proliferation of the electric driving, the new modes of vehicle use, and the autonomy and connectivity of vehicles represent new directions that challenge not only the automotive industry, but also vehicle users and regulators, and the states. New technologies bring about new security and safety challenges as well. Most of the challenges pop up in the cyber security domain. And its result is that a closer cooperation is necessary between the automotive industry and informatics. As these two leading industrial fields have a different setup, the cooperation is energy demanding task for all participants. Modification and upgrade of the homologation process seems to be one of the potential gateways that could merge the safety requests. Improving traditionally rigid automotive homologation processes needs a lot of extended test opportunities. In our research, we highlight the challenges posed by self-driving cars and show some trends briefly, and then examine the challenges posed by vehicle technology moving towards self-driving, and towards digitizing. The certification process of the automotive industry is highlighted and modifications are proposed. We propose to extend the traditional proving ground based certification processes with special, autonomous vehicles designed processes that are partially made within the virtual reality-proving ground mixtures. A newly designed proving ground not only offers a wide range of vehicle and traffic tests for conventional, connected and automated vehicles, but can also be used to test possible prototype solutions, as well as helps to develop the type-approval process, and useful for educational purposes. Cyber security has special dimensions, newly developed test environment is necessary to validate the vehicles and their elements. A complete vehicle testing and validation center is proposed to establish for automotive cyber security features, focusing not only on known, but also on unknown vulnerabilities. It will help to develop dedicated tests to eliminate unknown vulnerabilities and potential new vulnerabilities.

Planning the Future of Smart Cities With Swarms of Fully Autonomous Unmanned Aerial Vehicles Using a Novel Framework

The autonomy of unmanned aerial vehicles (UAVs) - self-governing in the aerospace discipline has been a remarkable research area with the development of the advanced bespoke microcontrollers embedded with advanced AI techniques for the last several decades. The road forward about the operational environment is certain about the swarms of fully automated UAVs (FAUAVs), that is, urban areas. FAUAVs with self-learning and self-decision-making abilities by executing non-trivial sequences of events with decimetre-level accuracy based on a set of rules, control loops and constraints using dynamic flight plans and trajectories are taking their indispensable parts within smart cities (SCs). Therefore, their integration with the SC components using real-time data analytics is urgent. This is mainly required to establish a better swarm intelligence along with a safer and optimised harmonious smart ecosystem that enables cooperative FAUAV-SC automation systems with collaborative automated intelligence engaging in the concepts of Internet of Everything (IoE) and Automation of Everything (AoE). Planning the future of cities with swarms of FAUAVs is explored in this paper to optimise the use of FAUAVs with a diverse range of applications and a contemporary methodology is proposed using a holistic framework - FAUAVinSCF equipped with various effective and efficient techniques along with a novel FAUAV routing technique customisable to the constraints of FAUAVs and urban areas. With the methodology, the components of SC and FAUAVs involving recent and impending technological advancements are moulded together to make this inevitable transformation a harmonious part of the inhabitants contributing to the cities' liveability and sustainability. The framework consists of a decentralized agent-based control architecture that monitors and controls the swarms of resource-constraint FAUAVs for their real-time requirements in optimising their urban uses. The outcomes of the methodology suggest that the constraints of FAUAVs can be mitigated significantly in urban areas and consequently, their efficacy can be increased in realising their diverse range of missions.

Predicting Intentions of Pedestrians from 2D Skeletal Pose Sequences with a Representation-Focused Multi-Branch Deep Learning Network

Understanding the behaviors and intentions of humans is still one of the main challenges for vehicle autonomy. More specifically, inferring the intentions and actions of vulnerable actors, namely pedestrians, in complex situations such as urban traffic scenes remains a difficult task and a blocking point towards more automated vehicles. Answering the question “Is the pedestrian going to cross?” is a good starting point in order to advance in the quest to the fifth level of autonomous driving. In this paper, we address the problem of real-time discrete intention prediction of pedestrians in urban traffic environments by linking the dynamics of a pedestrian’s skeleton to an intention. Hence, we propose SPI-Net (Skeleton-based Pedestrian Intention network): a representation-focused multi-branch network combining features from 2D pedestrian body poses for the prediction of pedestrians’ discrete intentions. Experimental results show that SPI-Net achieved 94.4% accuracy in pedestrian crossing prediction on the JAAD data set while being efficient for real-time scenarios since SPI-Net can reach around one inference every 0.25 ms on one GPU (i.e., RTX 2080ti), or every 0.67 ms on one CPU (i.e., Intel Core i7 8700K).

On the Road: Route Proposal from Radar Self-Supervised by Fuzzy LiDAR Traversability

This is motivated by a requirement for robust, autonomy-enabling scene understanding in unknown environments. In the method proposed in this paper, discriminative machine-learning approaches are applied to infer traversability and predict routes from Frequency-Modulated Contunuous-Wave (FMCV) radar frames. Firstly, using geometric features extracted from LiDAR point clouds as inputs to a fuzzy-logic rule set, traversability pseudo-labels are assigned to radar frames from which weak supervision is applied to learn traversability from radar. Secondly, routes through the scanned environment can be predicted after they are learned from the odometry traces arising from traversals demonstrated by the autonomous vehicle (AV). In conjunction, therefore, a model pretrained for traversability prediction is used to enhance the performance of the route proposal architecture. Experiments are conducted on the most extensive radar-focused urban autonomy dataset available to the community. Our key finding is that joint learning of traversability and demonstrated routes lends itself best to a model which understands where the vehicle should feasibly drive. We show that the traversability characteristics can be recovered satisfactorily, so that this recovered representation can be used in optimal path planning, and that an end-to-end formulation including both traversability feature extraction and routes learned by expert demonstration recovers smooth, drivable paths that are comprehensive in their coverage of the underlying road network. We conclude that the proposed system will find use in enabling mapless vehicle autonomy in extreme environments.

Incremento de la autonomía de un vehículo eléctrico Dayang CHOK-S a través de la utilización de paneles solares

La limitada autonomía de los vehículos eléctricos representa uno de los principales problemas para su comercialización. Por esta razón es importante incrementar su autonomía, generando de esta manera una alternativa de solución a estos inconvenientes. Es necesario entonces, promover la utilización de este tipo de automóviles, dado que su uso disminuye notablemente el grado de dependencia del petróleo, promoviendo así el uso de las energías renovables. La implementación de paneles solares en los automóviles eléctricos es una respuesta al incremento de su autonomía, ya que permite aprovechar una fuente inagotable de energía (solar), permitiendo de esta forma reducir la contaminación ambiental, haciendo que el vehículo eléctrico se vuelva más atractivo para los usuarios. Este artículo detalla la implementación de un panel solar de 350 W en un vehículo eléctrico Dayang CHOK-S bajo diferentes condiciones meteorológicas, obteniendo como resultado el incremento de su autonomía en hasta un 33 % con respecto a su autonomía real. Además, en algún punto dado, este porcentaje podría incrementar bajo condiciones de radiación solar bastante altas. Los resultados demuestran que la carga de las baterías es directamente proporcional a la cantidad de radiación solar; es decir, a mayor radiación solar, mayor va a ser la carga de las baterías del automóvil.

Theoretical study of SES solution for extending the electric vehicles autonomy

It is expected that in the near future the propulsion of motor vehicles will be mainly done using electric motors. The fundamental arguments are a much lower cost of travel and elimination of exhaust gases. However, the biggest problem of electric propulsion vehicles is the reduced autonomy. Regarding this disadvantage, there have been several attempts and they will continue to exist in order to eliminate or at least to reduce the phenomenon. Considering that, at present there is no technical solution showing real efficiency. A compromise can be achieved by using a small compact system based on a low cylinder thermal engine that trains an electric generator. This is achieved by means of a mechanical transmission called SES (Secondary Energy System). The main role is to charge the battery on board the vehicle, thus increasing its autonomy.

Investigating Inclusive Design of Shared Automated Vehicles with Full-Scale Modeling

Shared automated vehicles (SAVs) in the form of low-speed driverless shuttles have the potential to improve independent mobility for older adults and people with disabilities. At full vehicle autonomy and in the absence of an onboard operator, tasks such as ingress-egress, interior circulation, and securement of passengers and carry-on items will need to be safe, efficient, and independent. This paper describes a novel laboratory apparatus for conducting inclusive design research related to SAVs and presents preliminary findings from an ongoing preliminary study examining the effects of interior design configuration on ingress-egress performance for six wheelchair users. Early findings emphasize the interactions between diverse user abilities and technology design on user performance. The study demonstrates the potential benefit of full-scale physical simulations to investigating a broad range of usability and inclusive design issues related to emerging SAVs.

Fuel cell electric vehicles. Investigation of the energy balance for optimal reforming process of bio-ethanol

Hydrogen ecosystem and hydrogen economy are relevant topics for the mobility. This article summarizes the ways of production of “green” hydrogen. The hydrogen storage and transportation are discussed as well. The article presents the concept of electric vehicle with serial range extender to extend the vehicle autonomy. Fuel cell system powered by hydrogen produced through reforming of liquid fuel from renewable resources such as bioethanol is considered as a range extender module. Alkaline fuel cell and solid oxide fuel cell are investigated for the use of a variety of liquid fuels instead of traditionally used hydrogen in others types of fuel cells. This article investigates the energy balance of the different types of fuel cells, powered by bio-ethanol. Three variants for ethanol conversion are investigated: variant 1 : reforming of bio-ethanol in external reformer to hydrogen and its conversion in alkaline fuel cell, variant 2: reforming of bio-ethanol in external reformer to hydrogen and its conversion in a solid oxide fuel cell; variant 3 : direct reforming of the bio-ethanol in the solid oxide fuel cell. The chemical processes for each variant are proposed and thermodynamic energy balance is calculated. From the results is visible that the most efficient configuration is the variant 3, the direct reforming of the bio-ethanol by the solid oxide fuel cell. The variant 3 delivers 15 MJ of energy output per kilogram of bio-ethanol, used in the fuel cell.

A Robust Reactive Static Obstacle Avoidance System for Surface Marine Vehicles.

This paper is centered on the guidance systems used to increase the autonomy of unmanned surface vehicles (USVs). The new Robust Reactive Static Obstacle Avoidance System (RRSOAS) has been specifically designed for USVs. This algorithm is easily applicable, since previous knowledge of the USV mathematical model and its controllers is not needed. Instead, a new estimated closed-loop model (ECLM) is proposed and used to estimate possible future trajectories. Furthermore, the prediction errors (due to the uncertainty present in the ECLM) are taken into account by modeling the USV’s shape as a time-varying ellipse. Additionally, in order to decrease the computation time, we propose to use a variable prediction horizon and an exponential resolution to discretize the decision space. As environmental model an occupancy probability grid is used, which is updated with the measurements generated by a LIDAR sensor model. Finally, the new RRSOAS is compared with other SOA (static obstacle avoidance) methods. In addition, a robustness study was carried out over a set of random scenarios. The results obtained through numerical simulations indicate that RRSOAS is robust to unknown and congested scenarios in the presence of disturbances, while offering competitive performance with respect to other SOA methods.

Design and simulation studies of battery-supercapacitor hybrid energy storage system for improved performances of traction system of solar vehicle

Energy storage systems of Solar Vehicles require high energy density and high power density concurrently. The best solution is using supercapacitor (SC) during rapid power changes and in the recovery of braking energy to ameliorate solar vehicle autonomy. SCs can also keep batteries charged for extended durations and can be used to store the excess energy generated by photovoltaic panels. In this study, the power management of the electric traction system of the solar vehicle is insured by the hybridization of SCs and batteries to minimize the effects of peak current demands on the battery driving cycle. The efficiency of the overall system can be improved by the proposed hybrid storage system. The simulation results verify that integration of the SC into the photovoltaic energy storage system of the solar vehicle is effective in decreasing the battery stresses and eliminating the peak currents in the battery pack, thereby increasing the battery's life span. The use of SCs decreases the fluctuation in the direct current (DC) bus voltage during rapid motor speed changes and the photovoltaic fluctuations and provides a speed that is closest to the desired reference speed. A hardware design approach used for a small-scale prototype to proof the efficiency of the EMS and the distribution energy between batteries and SCs. It validated by three simulation tests.

Marine Robotics Competitions: a Survey.

Purpose of ReviewStarting from late 1990s, new marine robotics competitions have spread around the world. Today, the importance of competitions for both educational purposes and technological innovation is widely recognised by the community. In this paper, we present an overview of this kind of challenges, using the most representative events to describe common trends and unique aspects.Recent FindingsRecently, competitions have been in a rapid evolution. Trends are the increasing popularity of Grand Challenges, the push for autonomous decision-making, the introduction of multi-domain cooperation and the birth of virtual competitions. More attention has been given to the scientific aspects, stressing the benchmarking/metrological perspective. Finally, forums and workshops are currently seen as a way to complement competitions and to create a community of interest.SummaryMarine robotics competitions enable junior and expert teams to acquire experience in realistic environments. Virtual competitions can help in this aspect. They are useful for preparing team participation in the physical events, especially for open sea competitions. They allow teams to focus on advancing vehicle autonomy. Furthermore, they are important to widen the marine community attracting other robotics and AI experts.

300 GHz radar object recognition based on deep neural networks and transfer learning

For high-resolution scene mapping and object recognition, optical technologies such as cameras and LiDAR are the sensors of choice. However, for future vehicle autonomy and driver assistance in adverse weather conditions, improvements in automotive radar technology and the development of algorithms and machine learning for robust mapping and recognition are essential. In this study, the authors describe a methodology based on deep neural networks to recognise objects in 300 GHz radar images using the returned power data only, investigating robustness to changes in range, orientation and different receivers in a laboratory environment. As the training data is limited, they have also investigated the effects of transfer learning. As a necessary first step before road trials, they have also considered detection and classification in multiple object scenes.

Wireless Remote Control for Underwater Vehicles

Nowadays, the increasing availability of commercial off-the-shelf underwater acoustic and non-acoustic (e.g., optical and electromagnetic) modems that can be employed for both short-range broadband and long-range low-rate communication, the increasing level of autonomy of underwater vehicles, and the refinement of their underwater navigation systems pave the way for several new applications, such as data muling from underwater sensor networks and the transmission of real-time video streams underwater. In addition, these new developments inspired many companies to start designing hybrid wireless-driven underwater vehicles specifically tailored for off-shore operations and that are able to behave either as remotely operated vehicles (ROVs) or as autonomous underwater vehicles (AUVs), depending on both the type of mission they are required to perform and the limitations imposed by underwater communication channels. In this paper, we evaluate the actual quality of service (QoS) achievable with an underwater wireless-piloted vehicle, addressing the realistic aspects found in the underwater domain, first reviewing the current state-of-the-art of communication technologies and then proposing the list of application streams needed for control of the underwater vehicle, grouping them in different working modes according to the level of autonomy required by the off-shore mission. The proposed system is finally evaluated by employing the DESERT Underwater simulation framework by specifically analyzing the QoS that can be provided to each application stream when using a multimodal underwater communication system specifically designed to support different traffic-based QoSs. Both the analysis and the results show that changes in the underwater environment have a strong impact on the range and on the stability of the communication link.

The Effectiveness of Using a Pretrained Deep Learning Neural Networks for Object Classification in Underwater Video

Video image processing and object classification using a Deep Learning Neural Network (DLNN) can significantly increase the autonomy of underwater vehicles. This paper describes the results of a project focused on using DLNN for Object Classification in Underwater Video (OCUV) implemented in a Biomimetic Underwater Vehicle (BUV). The BUV is intended to be used to detect underwater mines, explore shipwrecks or observe the process of corrosion of munitions abandoned on the seabed after World War II. Here, the pretrained DLNNs were used for classification of the following type of objects: fishes, underwater vehicles, divers and obstacles. The results of our research enabled us to estimate the effectiveness of using pretrained DLNNs for classification of different objects under the complex Baltic Sea environment. The Genetic Algorithm (GA) was used to establish tuning parameters of the DLNNs. Three different training methods were compared for AlexNet, then one training method was chosen for fifteen networks and the tests were provided with the description of the final results. The DLNNs were trained on servers with six medium class Graphics Processing Units (GPUs). Finally, the trained DLNN was implemented in the Nvidia JetsonTX2 platform installed on board of the BUV, and one of the network was verified in a real environment.

Eco-Driving of Connected and Automated Vehicle With Preceding Driver Behavior Prediction

Abstract The development of vehicle connectivity and autonomy in the ground transportation sector is not only able to enhance traffic safety and driving comfort as well as fuel economy. This study presents a receding-horizon optimization-based control strategy integrated with the preceding vehicle speed prediction model to achieve an eco-driving strategy for connected and automated vehicles (CAVs). In the real traffic scenario where the CAV follows the preceding vehicle on the road, a gated recurrent unit (GRU) network is used to predict the behavior of the preceding vehicle by utilizing the historical inter-vehicle information collected through on-board sensors. Then, a nonlinear model predictive control (NMPC) algorithm is adopted for CAV to minimize the accumulated fuel consumption within the preview horizon. The NMPC approach solves the fuel-optimal speed profile of the CAV, considering a predicted short-term speed preview of the preceding vehicle. With the awareness of the preview speed conditions, the fuel consumption of the CAV is reduced by avoiding unnecessary braking and acceleration, especially during transient traffic conditions. The Pareto front framework is used to examine a trade-off between the vehicle speed prediction accuracy, computational burden, and the fuel consumption of the CAV in the proposed GRU-NMPC design. To analyze the effectiveness of the GRU-NMPC design, adaptive cruise control with constant time headway policy (ACC-CTH) is adopted as a benchmark control design. Comparison results show significant fuel economy improvement of the proposed design and expose possible fuel benefits from vehicle autonomy and sensor fusion technology.

It's Time to Rethink Levels of Automation for Self-Driving Vehicles [Opinion

Discusses issues involving the automation of self-driving vehicles. Reports on the technology of self-driving or autonomous automobiles. Examines the extent to which these vehicles serve the public interest as well as the level of consumer confidence in driving these vehicles. Suggests that self-driving cars could be a transformative technology in both good and bad ways. The important questions are not to do with when they will arrive but where, for whom, and in what forms they will appear. If we want a clearer sense of the possibilities from automated vehicle systems, we need to broaden our gaze [3]. Rather than emphasizing the autonomy of self-driving vehicles, we should instead be talking about their conditionality. We need to know about the circumstances in which different systems could have an impact on our lives. Self-driving vehicle systems will serve different purposes and take on different shapes in different places. A schema for innovation that points in one direction and says nothing about the desirability of the destination makes for a poor roadmap.

Autonomous vehicles and cycling: Policy implications and management issues

Cycling as a form of active transport has great benefits for individuals and society, yet prevalence rates in many countries are low. The advent of autonomous vehicles (AVs) is likely to have substantial implications for cyclists, however little is known about the nature and magnitude of the likely impacts and the resulting implications for government planning and policy. The aim of this exploratory study was to consult with a range of stakeholder groups to identify relevant issues and stimulate debate about future efforts to maximize the benefits of vehicle autonomy for cycling outcomes. Interviews were conducted with key stakeholders representing government (local, state, and federal departments responsible for transport, health, and/or infrastructure), cycling organizations, technology firms, AV manufacturing/servicing companies, trade unions, the law, insurers (public and private), transport policy consortia, and academia. The results suggest that AVs have the potential to increase cycling prevalence while reducing cycling accidents, but that a range of issues will need to be addressed to optimize these outcomes. In particular, informed decisions need to be made about the infrastructure and equipment investments that could encourage larger numbers of commuters to select cycling as their primary form of transport.

Development of autonomous navigation controller for agricultural vehicles

Agricultural vehicles are adopted to undertake farming tasks by traversing along crop rows in the field. Working quality depends significantly on the driving skills of the operator. Automatic guidance has been introduced into agriculture to achieve high-accuracy path tracking during the last decades, which contributes considerably to straight-line navigation. The objective of this research was to develop an autonomous navigation controller that allowed movement autonomy for various agricultural vehicles. Three wheel-type vehicles were used as the test platform featuring automatic steering, hydrostatic transmission and speed control, which included a rice transplanter, a high-clearance sprayer and a tractor. A dual-antenna RTK-GNSS receiver was attached to the vehicles to provide spatial information on both positioning and heading by using the RTX service from Trimble. A path planning method was proposed to create a straight-line reference path by giving two points, and the target path was determined according to the vehicle initial status and working assignment. Headland turning was comprehensively taken into account by listing different turn patterns in order to realize autonomous navigation at the headland. The navigation controller hardware was fabricated for program execution, data processing and information communication with peripherals. A human-machine interface was designed for the operator to complete basic setting, path planning and navigation control by providing controls. Field experiments were conducted to evaluate the performance and versatility of the newly developed autonomous navigation controller in guiding agricultural vehicles to follow straight paths and turn at the headland. Results showed that an appropriate turn pattern was automatically executed when finishing straight-line navigation. The lateral error in straight-line tracking was no more than 6 cm, 6 cm and 5 cm for the rice transplanter, the high-clearance sprayer and the tractor, respectively. And the maximum lateral RMS error was 3.10 cm, 4.75 cm, 2.21 cm in terms of straight-line tracking, which indicated that the newly developed autonomous navigation controller was versatile and of high robustness in guiding various agricultural vehicles. Keywords: autonomous navigation, navigation controller, agricultural vehicles, straight-line tracking, straight path, headland turning DOI: 10.25165/j.ijabe.20201304.5470 Citation: Yin X, Wang Y X, Chen Y L, Jin C Q, Du J. Development of autonomous navigation controller for agricultural vehicles. Int J Agric & Biol Eng, 2020; 13(4): 70–76.