1,489 research outputs found

    Ensuring Safe and Robust Human-Machine Interaction in Autonomous Electric Vehicles: State-of-the-Art Techniques

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    Autonomous electric vehicles (AEVs) are gaining popularity due to their potential to reduce accidents caused by human error and decrease carbon emissions. However, ensuring safe and robust human-machine interaction in AEVs remains a significant challenge. To address this challenge, we reviewed several state-of-the-art techniques currently being developed and implemented. Our findings show that AEVs rely on a range of sensors and perception systems, including cameras, lidars, radars, and GPS, to detect and respond to their environment. Advanced perception algorithms and machine learning techniques are used to process the data collected by these sensors and provide real-time information about the vehicle's surroundings. The human-machine interface (HMI) is the primary means of interaction between the vehicle and the passenger, and it should be designed to be intuitive, informative, and easy to use. Artificial intelligence and machine learning algorithms are used to make decisions and adapt to changing road conditions. Cybersecurity measures, such as encryption, authentication, and intrusion detection, are essential to prevent cyberattacks on AEVs. Redundancy and fail-safe systems, including redundant sensors, processors, communication systems, backup power sources, and emergency braking systems, ensure that AEVs can continue to operate safely in the event of a failure or malfunction. Finally, rigorous testing and validation are necessary to ensure that AEVs meet safety standards and perform as intended. Our review provides valuable insights into the state-of-the-art techniques for ensuring robust and safe human-machine interaction in AEVs, which can guide future research and development in this area

    ๊ณ ์„ฑ๋Šฅ ํ•œ๊ณ„ ํ•ธ๋“ค๋ง์„ ์œ„ํ•œ ์ธํœ ๋ชจํ„ฐ ํ† ํฌ๋ฒกํ„ฐ๋ง ์ œ์–ด

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    ํ•™์œ„๋…ผ๋ฌธ(๋ฐ•์‚ฌ) -- ์„œ์šธ๋Œ€ํ•™๊ต๋Œ€ํ•™์› : ๊ณต๊ณผ๋Œ€ํ•™ ๊ธฐ๊ณ„ํ•ญ๊ณต๊ณตํ•™๋ถ€, 2021.8. ์ด๊ฒฝ์ˆ˜.์ง€๋‚œ 10๋…„ ๋™์•ˆ ์ฐจ๋Ÿ‰ ์ž์„ธ ์ œ์–ด์‹œ์Šคํ…œ(ESC)์€ ์น˜๋ช…์ ์ธ ์ถฉ๋Œ์„ ๋ฐฉ์ง€ํ•˜๊ธฐ ์œ„ํ•ด ๋งŽ์€ ์ƒ์šฉ ์ฐจ๋Ÿ‰์—์„œ ๋น„์•ฝ์ ์œผ๋กœ ๋ฐœ์ „๋˜๊ณ  ๊ฐœ๋ฐœ๋˜๊ณ  ์žˆ๋‹ค. ํŠนํžˆ, ์ฐจ๋Ÿ‰ ์ž์„ธ ์ œ์–ด ์‹œ์Šคํ…œ์€ ์•…์ฒœํ›„๋กœ ์ธํ•œ ๋ฏธ๋„๋Ÿฌ์šด ๋„๋กœ์™€ ๊ฐ™์€ ์œ„ํ—˜ํ•œ ๋„๋กœ์—์„œ ๋ถˆ์•ˆ์ •ํ•œ ์ฐจ๋Ÿ‰ ์ฃผํ–‰ ์กฐ๊ฑด์—์„œ ์‚ฌ๊ณ ๋ฅผ ํ”ผํ•˜๋Š”๋ฐ ํฐ ์—ญํ• ์„ ํ•œ๋‹ค. ๊ทธ๋Ÿฌ๋‚˜, ์ตœ๊ทผ์˜ ๊ฒฝ์šฐ, ๊ณ ์„ฑ๋Šฅ ์ฐจ๋Ÿ‰ ๋˜๋Š” ์Šคํฌ์ธ ์นด ๋“ฑ์˜ ๊ฒฝ์šฐ ์ œ๋™์ œ์–ด์˜ ๋นˆ๋ฒˆํ•œ ๊ฐœ์ž…์€ ์šด์ „์˜ ์ฆ๊ฑฐ์›€์„ ๊ฐ์†Œ์‹œํ‚ค๋Š” ๋ถˆ๋งŒ๋„ ์กด์žฌํ•œ๋‹ค. ์ตœ๊ทผ ์ฐจ๋Ÿ‰์˜ ์ „๋™ํ™”์™€ ํ•จ๊ป˜, ์ž๋Ÿ‰ ์ž์„ธ ์ œ์–ด์‹œ์Šคํ…œ์˜ ์ž‘๋™ ์˜์—ญ์ธ ํ•œ๊ณ„ ์ฃผํ–‰ ํ•ธ๋“ค๋ง ์กฐ๊ฑด์—์„œ ๊ฐ ํœ ์˜ ๋…๋ฆฝ์ ์ธ ๊ตฌ๋™์„ ์ ์šฉ ํ•  ์ˆ˜ ์žˆ๋Š” ์‹œ์Šคํ…œ ์ค‘ ํ•˜๋‚˜์ธ ์ธํœ  ๋ชจํ„ฐ ์‹œ์Šคํ…œ์„ ์‚ฌ์šฉํ•˜์—ฌ ์ฐจ๋Ÿ‰์˜ ์ข…, ํšก๋ฐฉํ–ฅ ํŠน์„ฑ์„ ์ œ์–ด ๊ฐ€๋Šฅํ•˜๊ฒŒ ํ•˜๋Š” ํ† ํฌ ๋ฒกํ„ฐ๋ง ์ œ์–ด๊ธฐ์ˆ ์— ๋Œ€ํ•œ ์—ฐ๊ตฌ๊ฐ€ ํ™œ๋ฐœํ•˜๋‹ค. ๋”ฐ๋ผ์„œ, ๋ณธ ์—ฐ๊ตฌ์—์„œ๋Š” ์ฐจ๋Ÿ‰์˜ ์„ ํšŒ ํ•œ๊ณ„ ํ•ธ๋“ค๋ง ์กฐ๊ฑด์—์„œ ์•ˆ์ •์„ฑ๊ณผ ์ฃผํ–‰ ๋‹ค์ด๋‚˜๋ฏน ์„ฑ๋Šฅ์„ ํ–ฅ์ƒ์‹œํ‚ฌ ์ˆ˜ ์žˆ๋Š” ํ† ํฌ ๋ฒกํ„ฐ๋ง ์ œ์–ด๊ธฐ๋ฅผ ์ œ์•ˆํ•˜๊ณ ์ž ํ•œ๋‹ค. ๋จผ์ €, ์ฐจ๋Ÿ‰์˜ ๋น„์„ ํ˜• ์ฃผํ–‰ ๊ตฌ๊ฐ„์ธ ํ•œ๊ณ„ ํ•ธ๋“ค๋ง ์กฐ๊ฑด์— ๋Œ€ํ•œ ์ž๋™ ๋“œ๋ฆฌํ”„ํŠธ ์ œ์–ด ์•Œ๊ณ ๋ฆฌ์ฆ˜์„ ์ œ์•ˆํ•œ๋‹ค. ์ด ์•Œ๊ณ ๋ฆฌ์ฆ˜์„ ์ด์šฉํ•˜์—ฌ ํ† ํฌ๋ฒกํ„ฐ๋ง์ œ์–ด์— ์ฐจ๋Ÿ‰์˜ ๋‹ค์ด๋‚˜๋ฏนํ•œ ์ฃผํ–‰๋ชจ๋“œ์— ๋Œ€ํ•œ ํ†ต์ฐฐ๋ ฅ์„ ์ œ๊ณตํ•˜๊ณ  ๋ฏธ๋„๋Ÿฌ์šด ๋„๋กœ์—์„œ ์ฐจ๋Ÿ‰์˜ ๋†’์€ ์Šฌ๋ฆฝ ๊ฐ๋„์˜ ์•ˆ์ •์„ฑ ์ œ์–ด๋ฅผ ์ œ๊ณต ํ•  ์ˆ˜ ์žˆ๋‹ค. ๋˜ํ•œ, ์ธํœ  ๋ชจํ„ฐ ์‹œ์Šคํ…œ์„ ์ฐจ๋Ÿ‰์˜ ์ „๋ฅœ์— 2๊ฐœ ๋ชจํ„ฐ๋กœ ์‚ฌ์šฉํ•˜์—ฌ ์ฐจ๋Ÿ‰ ๊ณ ์œ ์˜ ํŠน์„ฑ์ธ ์ฐจ๋Ÿ‰ ์–ธ๋”์Šคํ‹ฐ์–ด ๊ตฌ๋ฐฐ๋ฅผ ์ง์ ‘์  ์ œ์–ด๋ฅผ ์ˆ˜ํ–‰ํ•˜์—ฌ, ์ฐจ๋Ÿ‰์˜ ํ•ธ๋“ค๋ง ์„ฑ๋Šฅ์„ ํ–ฅ์ƒ์‹œ์ผฐ๋‹ค. ์ œ์–ด๊ธฐ์˜ ์ฑ„ํ„ฐ๋ง ํšจ๊ณผ๋ฅผ ์ค„์ด๊ณ  ๋น ๋ฅธ ์‘๋‹ต์„ ์–ป๊ธฐ ์œ„ํ•ด ์ƒˆ๋กœ์šด ๊ณผ๋„ ๋งค๊ฐœ ๋ณ€์ˆ˜๊ฐ€ ์ด์šฉํ•˜์—ฌ ์ˆ˜์‹ํ™”ํ•˜์—ฌ ๊ตฌ์„ฑํ•˜์˜€์œผ๋ฉฐ, ์ฐจ๋Ÿ‰์˜ ์ •์ƒ ์ƒํƒœ ๋ฐ ๊ณผ๋„ ํŠน์„ฑ ํ–ฅ์ƒ์„ ๊ฒ€์ฆํ•˜๊ธฐ ์œ„ํ•˜์—ฌ ISO ๊ธฐ๋ฐ˜ ์‹œ๋ฎฌ๋ ˆ์ด์…˜ ๋ฐ ์ฐจ๋Ÿ‰ ์‹คํ—˜์„ ์ˆ˜ํ–‰ํ•˜์˜€๋‹ค. ๋งˆ์ง€๋ง‰์œผ๋กœ ์š” ์ œ์–ด๊ธฐ์™€ ํšก ์Šฌ๋ฆฝ ๊ฐ๋„ ์ œ์–ด๊ธฐ๋กœ ๊ตฌ์„ฑ๋œ MASMC (Multiple Adaptive Sliding Mode Control) ์ ‘๊ทผ ๋ฐฉ์‹์„ ์‚ฌ์šฉํ•˜๋Š” 4๋ฅœ ๋ชจํ„ฐ ์‹œ์Šคํ…œ์„ ์‚ฌ์šฉํ•œ ๋™์  ํ† ํฌ๋ฒกํ„ฐ๋ง ์ œ์–ด๋ฅผ ์ˆ˜ํ–‰ํ•˜์˜€๋‹ค. ๋†’์€ ๋น„์„ ํ˜• ํŠน์„ฑ์„ ๊ฐ€์ง„ ์ฐจ๋Ÿ‰์˜ ์ „ํ›„๋ฅœ ํƒ€์ด์–ด์˜ ์ฝ”๋„ˆ๋ง ๊ฐ•์„ฑ์€ ์ ์‘์ œ์–ด๊ธฐ๋ฒ•์„ ์ด์šฉํ•˜์—ฌ ์˜ˆ์ธกํ•˜์˜€๋‹ค. ๋”ฐ๋ผ์„œ, ์•ˆ์ „๋ชจ๋“œ์™€ ๋‹ค์ด๋‚˜๋ฏน ๋ชจ๋“œ๋ฅผ ๊ตฌ์„ฑํ•˜์—ฌ, ์šด์ „์ž๋กœ ํ•˜์—ฌ๊ธˆ ์›ํ•˜๋Š” ์ฃผํ–‰์˜ ์กฐ๊ฑด์— ๋งž๊ฒŒ ์„ ํƒํ•  ์ˆ˜ ์žˆ๋Š” ์•Œ๊ณ ๋ฆฌ์ฆ˜์„ ๊ตฌํ˜„ํ•˜์˜€๋‹ค. ์ด MASMC ์•Œ๊ณ ๋ฆฌ์ฆ˜์€ ํ–ฅํ›„ ์ „๋™ํ™” ์ฐจ๋Ÿ‰์— ์ฃผํ–‰์•ˆ์ •์„ฑ ํ–ฅ์ƒ๊ณผ ๋‹ค์ด๋‚˜๋ฏนํ•œ ์ฃผํ–‰์˜ ์ฆ๊ฑฐ์›€์„ ์ฃผ๋Š” ๊ธฐ์ˆ ๋กœ์จ, ์ „์ฐจ๋Ÿ‰ ์‹œ๋ฎฌ๋ ˆ์ด์…˜์„ ์ด์šฉํ•˜์—ฌ ๊ฒ€์ฆํ•˜์˜€๋‹ค.In the last ten decades, vehicle stability control systems have been dramatically developed and adapted in many commercial vehicles to avoid fatal crashes. Significantly, ESC (Electric Stability Control) system can help escape the accident from unstable driving conditions with dangerous roads such as slippery roads due to inclement weather conditions. However, for the high performed vehicle, frequent intervention from ESC reduces the pleasure of fun-to-drive. Recently, the development of traction control technologies has been taking place with that of the electrification of vehicles. The IWMs (In-Wheel Motor system), which is one of the systems that can apply independent drive of each wheel, for the limit handling characteristics, which are the operation areas of the ESC, is introduced for the control that enables the lateral characteristics of the vehicle dynamics. Firstly, the automated drift control algorithm can be proposed for the nonlinear limit handling condition of vehicles. This approach can give an insight of fun-to-drive mode to TV (Torque Vector) control scheme, but also the stability control of high sideslip angle of the vehicle on slippery roads. Secondly, using IWMs system with front two motors, understeer gradient of vehicle, which is the unique characteristics of vehicle can be used for the proposed control strategy. A new transient parameter is formulated to be acquired rapid response of controller and reducing chattering effects. Simulation and vehicle tests are conducted for validation of TV control algorithm with steady-state and transient ISO-based tests. Finally, dynamic torque vectoring control with a four-wheel motor system with Multiple Adaptive Sliding Mode Control (MASMC) approach, which is composed of a yaw rate controller and sideslip angle controller, is introduced. Highly nonlinear characteristics, cornering stiffnesses of front and rear tires are estimated by adaptation law with measuring data. Consequently, there are two types of driving modes, the safety mode and the dynamic mode. MASMC algorithm can be found and validated by simulation in torque vectoring technology to improve the handling performance of fully electric vehicles.Chapter 1 Introduction 7 1.1. Background and Motivation 7 1.2. Literature review 11 1.3. Thesis Objectives 15 1.4. Thesis Outline 15 Chapter 2 Vehicle dynamic control at limit handling 17 2.1. Vehicle Model and Analysis 17 2.1.1. Lateral dynamics of vehicle 17 2.1.2. Longitudinal dynamics of vehicle 20 2.2. Tire Model 24 2.3. Analysis of vehicle drift for fun-to-drive 28 2.4. Designing A Controller for Automated Drift 34 2.4.1. Lateral controller 35 2.4.2. Longitudinal Controller 37 2.4.3. Stability Analysis 39 2.4.4. Validation with simulation and test 40 Chapter 3 Torque Vectoring Control with Front Two Motor In-Wheel Vehicles 47 3.1. Dynamic Torque Vectoring Control 48 3.1.1. In-wheel motor system (IWMs) 48 3.1.2. Dynamic system modeling 49 3.1.3. Designing controller 53 3.2. Validation with Simulation and Experiment 59 3.2.1. Simulation 59 3.2.2. Vehicle Experiment 64 Chapter 4 Dynamic handling control for Four-wheel Drive In-Wheel platform 75 4.1. Vehicle System Modeling 76 4.2. Motion Control based on MASMC 78 4.2.1. Yaw motion controller for the inner ASMC 80 4.2.2. Sideslip angle controller for the outer ASMC 84 4.3. Optimal Torque Distribution (OTD) 88 4.3.1. Constraints of dynamics 88 4.3.2. Optimal torque distribution law 90 4.4. Validation with Simulation 91 4.4.1. Simulation setup 91 4.4.2. Simulation results 92 Chapter 5 Conclusion and Future works 104 5.1 Conclusion 104 5.2 Future works 106 Bibliography 108 Abstract in Korean 114๋ฐ•

    Electromechanical systems with transient high power response operating from a resonant AC link

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    The combination of an inherently robust asynchronous (induction) electrical machine with the rapid control of energy provided by a high frequency resonant AC link enables the efficient management of higher power levels with greater versatility. This could have a variety of applications from launch vehicles to all-electric automobiles. These types of systems utilize a machine which is operated by independent control of both the voltage and frequency. This is made possible by using an indirect field-oriented control method which allows instantaneous torque control in all four operating quadrants. Incorporating the AC link allows the converter in these systems to switch at the zero crossing of every half cycle of the AC waveform. This zero loss switching of the link allows rapid energy variations to be achieved without the usual frequency proportional switching loss. Several field-oriented control systems were developed by LeRC and General Dynamics Space Systems Division under contract to NASA. A description of a single motor, electromechanical actuation system is presented. Then, focus is on a conceptual design for an AC electric vehicle. This design incorporates an induction motor/generator together with a flywheel for peak energy storage. System operation and implications along with the associated circuitry are addressed. Such a system would greatly improve all-electric vehicle ranges over the Federal Urban Driving Cycle (FUD)

    Improvement of the break and steering system for the golf-cart

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    This project aims to gain knowledge and test theories related to autonomous vehicles using an electric golf cart as a test platform. There are several goals set, including obtaining electrical and controls experience related to autonomous driving and expanding their electrical engineering skills. The project has the potential to benefit the general population, as the electric golf cart shares similar attributes to a motor vehicle. Through diligent work and research, three key objectives have been achieved: transforming the previous single Arduino system into two mega Arduinos, reconfiguring the placement of the steering motor, and implementing an emergency braking system. Improving the golf cart\u27s steering, voltage regulator throttle, and braking system using sensors to address existing complications. The primary goal is to use remote control to test applications instead of LiDAR, supporting future plans to make the project autonomous

    Optimal Torque Control for an Electric-Drive Vehicle with In-Wheel Motors: Implementation and Experiments

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    ยฉ SAE, Athari, A., Fallah, S., Li, B., Khajepour, A. et al., "Optimal Torque Control for an Electric-Drive Vehicle with In-Wheel Motors: Implementation and Experiments," SAE Int. J. Commer. Veh. 6(1):82-92, 2013, doi:10.4271/2013-01-0674.This paper presents the implementation of an off-line optimized torque vectoring controller on an electric-drive vehicle with four in-wheel motors for driver assistance and handling performance enhancement. The controller takes vehicle longitudinal, lateral, and yaw acceleration signals as feedback using the concept of state-derivative feedback control. The objective of the controller is to optimally control the vehicle motion according to the driver commands. Reference signals are first calculated using a driver command interpreter to accurately interpret what the driver intends for the vehicle motion. The controller then adjusts the braking/throttle outputs based on discrepancy between the vehicle response and the interpreter command. A test vehicle equipped with four in-wheel electric motors, vehicle sensors, communication buses, and dSPACE rapid prototyping hardware is instrumented and the control performance is verified through vehicle handling tests under different driving conditions.Automotive Partnership CanadaOntario Research FundGeneral Motor

    Intelligent automatic overtaking system using vision for vehicle detection

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    There is clear evidence that investment in intelligent transportation system technologies brings major social and economic benefits. Technological advances in the area of automatic systems in particular are becoming vital for the reduction of road deaths. We here describe our approach to automation of one the riskiest autonomous manล“uvres involving vehicles โ€“ overtaking. The approach is based on a stereo vision system responsible for detecting any preceding vehicle and triggering the autonomous overtaking manล“uvre. To this end, a fuzzy-logic based controller was developed to emulate how humans overtake. Its input is information from the vision system and from a positioning-based system consisting of a differential global positioning system (DGPS) and an inertial measurement unit (IMU). Its output is the generation of action on the vehicleโ€™s actuators, i.e., the steering wheel and throttle and brake pedals. The system has been incorporated into a commercial Citroรซn car and tested on the private driving circuit at the facilities of our research center, CAR, with different preceding vehicles โ€“ a motorbike, car, and truck โ€“ with encouraging results

    Development of a modular dual engine hybrid electric vehicle simulation model

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    Depleting resources of fossil fuel, climate change impacts, high oil prices, and strict emission requirements are leading to the research on efficient, environmentally friendly, and lowered fossil fuel dependent solutions in the transportation field. While a number of studies used computer modeling and simulation tools to investigate hybrid electric vehicles (HEVs), very few attempted to model and simulate a dual-engine hybrid vehicle. Designing a vehicle engine to meet energy needs in the fully loaded condition is not an optimal solution for manufacturers and customers. The larger the engine, the higher the manufacturing costs for companies, and higher fuel consumption for customers. The integration of dual-engine hybrid technology can help to solve this problem. The objective of this study was to design and simulate a dual-engine hybrid electric vehicle (DE-HEV) model to investigate whether it can be a fuel efficient and environmentally friendly solution without sacrificing vehicle performance. The simulated DE-HEV uses two small engines instead of one large engine. In the simulated design, a smaller single engine supplies the power if the energy need is not more than a single engine can provide. The second engine turns on when the power demand is greater than the single engine can supply. Working models for the DE-HEV components, such as an electric motor, generator, battery, and the controller have been developed using the Matlab/Simulinkโ„ข simulation package. Each model was validated with test data from the literature. Appropriate power management strategy has been developed to accommodate the dual engine design. Fuel-efficiency, overall performance, and manufacturing cost for the simulated DE-HEV model have been compared against current commercial models. Simulation results showed that DE-HEV has between a 2% to 6% higher efficiency than comparable HEVs. Cost analysis results showed that the manufacturing cost of DE-HEV is 11% higher. Performance of the vehicle was tested with standard drive cycles. Test results are satisfactory; although there was significant increase in fuel-efficiency, because of its higher initial manufacturing cost, maintenance, and complexity, DE-HEVs may have challenges in the short term. However, with expected decreases in manufacturing costs of battery storage and power electronics technology, the implementation of DE-HEVs can be feasible transportation options in the near future
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