Actuators for Intelligent Electric Vehicles

This book details the advanced actuators for IEVs and the control algorithm design. In the actuator design, the configuration four-wheel independent drive/steering electric vehicles is reviewed. An in-wheel two-speed AMT with selectable one-way clutch is designed for IEV. Considering uncertainties, the optimization design for the planetary gear train of IEV is conducted. An electric power steering system is designed for IEV. In addition, advanced control algorithms are proposed in favour of active safety improvement. A supervision mechanism is applied to the segment drift control of autonomous driving. Double super-resolution network is used to design the intelligent driving algorithm. Torque distribution control technology and four-wheel steering technology are utilized for path tracking and adaptive cruise control. To advance the control accuracy, advanced estimation algorithms are studied in this book. The tyre-road peak friction coefficient under full slip rate range is identified based on the normalized tyre model. The pressure of the electro-hydraulic brake system is estimated based on signal fusion. Besides, a multi-semantic driver behaviour recognition model of autonomous vehicles is designed using confidence fusion mechanism. Moreover, a mono-vision based lateral localization system of low-cost autonomous vehicles is proposed with deep learning curb detection. To sum up, the discussed advanced actuators, control and estimation algorithms are beneficial to the active safety improvement of IEVs

Trends in vehicle motion control for automated driving on public roads

In this paper, we describe how vehicle systems and the vehicle motion control are affected by automated driving on public roads. We describe the redundancy needed for a road vehicle to meet certain safety goals. The concept of system safety as well as system solutions to fault tolerant actuation of steering and braking and the associated fault tolerant power supply is described. Notably restriction of the operational domain in case of reduced capability of the driving automation system is discussed. Further we consider path tracking, state estimation of vehicle motion control required for automated driving as well as an example of a minimum risk manoeuver and redundant steering by means of differential braking. The steering by differential braking could offer heterogeneous or dissimilar redundancy that complements the redundancy of described fault tolerant steering systems for driving automation equipped vehicles. Finally, the important topic of verification of driving automation systems is addressed

Integrated trajectory planning and control for obstacle avoidance manoeuvre using nonlinear vehicle model-predictive algorithm

In the current literature, model-predictive (MP) algorithm is widely applied in autonomous vehicle trajectory planning and control but most of the current studies only apply the linear tyre model, which cannot accurately present the tyre non-linear characteristic. Furthermore, most of these studies separately consider the trajectory planning and trajectory control of the autonomous vehicle and few of them have integrated the trajectory planning and trajectory control together. To fill in above research gaps, this study proposes the integrated trajectory planning and trajectory control method using a non-linear vehicle MP algorithm. To fully utilise the advantages of four-wheel-independent-steering and four-wheel-independent-driving vehicle, the MP algorithm is proposed based on four-wheel dynamics model and non-linear Dugoff tyre model. This study also proposes the mathematical modelling of the static obstacle and dynamic obstacle for the obstacle avoidance manoeuvre of the autonomous vehicle. Finally, simulation results have been presented to show the effectiveness of the proposed control method

Advanced Control and Estimation Concepts, and New Hardware Topologies for Future Mobility

According to the National Research Council, the use of embedded systems throughout society could well overtake previous milestones in the information revolution. Mechatronics is the synergistic combination of electronic, mechanical engineering, controls, software and systems engineering in the design of processes and products. Mechatronic systems put “intelligence” into physical systems. Embedded sensors/actuators/processors are integral parts of mechatronic systems. The implementation of mechatronic systems is consistently on the rise. However, manufacturers are working hard to reduce the implementation cost of these systems while trying avoid compromising product quality. One way of addressing these conflicting objectives is through new automatic control methods, virtual sensing/estimation, and new innovative hardware topologies

A Systematic Survey of Control Techniques and Applications: From Autonomous Vehicles to Connected and Automated Vehicles

Vehicle control is one of the most critical challenges in autonomous vehicles (AVs) and connected and automated vehicles (CAVs), and it is paramount in vehicle safety, passenger comfort, transportation efficiency, and energy saving. This survey attempts to provide a comprehensive and thorough overview of the current state of vehicle control technology, focusing on the evolution from vehicle state estimation and trajectory tracking control in AVs at the microscopic level to collaborative control in CAVs at the macroscopic level. First, this review starts with vehicle key state estimation, specifically vehicle sideslip angle, which is the most pivotal state for vehicle trajectory control, to discuss representative approaches. Then, we present symbolic vehicle trajectory tracking control approaches for AVs. On top of that, we further review the collaborative control frameworks for CAVs and corresponding applications. Finally, this survey concludes with a discussion of future research directions and the challenges. This survey aims to provide a contextualized and in-depth look at state of the art in vehicle control for AVs and CAVs, identifying critical areas of focus and pointing out the potential areas for further exploration

자율 주행 차량의 심층강화학습 기반 긴급 차선 변경 경로 최적화

학위논문(박사) -- 서울대학교대학원 : 공과대학 기계항공공학부, 2021.8. 최예림.The emergency lane change is a risk itself because it is made instantaneously in emergency such as a sudden stop of the vehicle in front in the driving lane. Therefore, the optimization of the lane change trajectory is an essential research area of autonomous vehicle. This research proposes a path optimization for emergency lane change of autonomous vehicles based on deep reinforcement learning. This algorithm is developed with a focus on fast and safe avoidance behavior and lane change in an emergency. As the first step of algorithm development, a simulation environment was established. IPG CARMAKER was selected for reliable vehicle dynamics simulation and construction of driving scenarios for reinforcement learning. This program is a highly reliable and can analyze the behavior of a vehicle similar to that of a real vehicle. In this research, a simulation was performed using the Hyundai I30-PDe full car model. And as a simulator for DRL and vehicle control, Matlab Simulink which can encompass all of control, measurement, and artificial intelligence was selected. By connecting two simulators, the emergency lane change trajectory is optimized based on DRL. The vehicle lane change trajectory is modeled as a 3rd order polynomial. The start and end point of the lane change is set and analyzed as a function of the lane change distance for the coefficient of the polynomial. In order to optimize the coefficients. A DRL architecture is constructed. 12 types of driving environment data are used for the observation space. And lane change distance which is a variable of polynomial is selected as the output of action space. Reward space is designed to maximize the learning ability. Dynamic & static reward and penalty are given at each time step of simulation, so that optimization proceeds in a direction in which the accumulated rewards could be maximized. Deep Deterministic Policy Gradient agent is used as an algorithm for optimization. An algorithm is developed for driving a vehicle in a dynamic simulation program. First, an algorithm is developed that can determine when, at what velocity, and in which direction to change the lane of a vehicle in an emergency situation. By estimating the maximum tire-road friction coefficient in real-time, the minimum distance for the driving vehicle to stop is calculated to determine the risk of longitudinal collision with the vehicle in front. Also, using Gipps’ safety distance formula, an algorithm is developed that detects the possibility of a collision with a vehicle coming from the lane to be changed, and determines whether to overtake the vehicle to pass forward or to go backward after as being overtaken. Based on this, the decision-making algorithm for the final lane change is developed by determine the collision risk and safety of the left and right lanes. With the developed algorithm that outputs the emergency lane change trajectory through the configured reinforcement learning structure and the general driving trajectory such as the lane keeping algorithm and the adaptive cruise control algorithm according to the situation, an integrated algorithm that drives the ego vehicle through the adaptive model predictive controller is developed. As the last step of the research, DRL was performed to optimize the developed emergency lane change path optimization algorithm. 60,000 trial-and-error learning is performed to develop the algorithm for each driving situation, and performance is evaluated through test driving.긴급 차선 변경은 주행 차선에서 선행차량 급정거와 같은 응급상황 발생시에 순간적으로 이루어지는 것이므로 그 자체에 위험성을 안고 있다. 지나치게 느리게 조향을 하는 경우, 주행 차량은 앞에 있는 장애물과의 충돌을 피할 수 없다. 이와 반대로 지나치게 빠르게 조향을 하는 경우, 차량과 지면 사이의 작용력은 타이어 마찰 한계를 넘게 된다. 이는 차량의 조종 안정성을 떨어트려 스핀이나 전복 등 다른 양상의 사고를 야기한다. 따라서 차선 변경 경로의 최적화는 자율 주행 차량의 응급 상황 대처에 필수적인 요소이다. 본 논문에서는 심층강화학습을 기반으로 자율 주행 차량의 긴급 차선 변경 경로를 최적화한다. 이 알고리즘은 선행차량의 급정거나 장애물 출현과 같은 응급상황 발생 시, 빠르고 안전한 회피 거동 및 차선 변경에 초점을 맞추어 개발되었다. 알고리즘 개발의 첫 번째 단계로서 시뮬레이션 환경이 구축되었다. 신뢰성 있는 차량 동역학 시뮬레이션과 강화학습을 위한 주행 시나리오 구축을 위하여 IPG CARMAKER가 선정되었다. 이 프로그램은 실제 산업 현장에서 사용되는 높은 신뢰성을 가진 프로그램으로 실제 차량과 유사한 차량의 거동을 분석할 수 있다. 본 연구에서는 현대자동차의 I30-PDe 모델을 사용하여 시뮬레이션을 수행하였다. 또한 강화학습과 차량제어를 위한 프로그램으로 제어, 계측, 인공지능을 모두 아우를 수 있는 Matlab Simulink를 선정하였다. 본 연구에서는 IPG CARMAKER와 Matlab Simulink를 연동하여 심층 강화 학습을 바탕으로 긴급 차선 변경 궤적을 최적화하였다. 차량의 차선 변경 궤적은 3차 다항식의 형상으로 모델링 되었다. 차선 변경 시작 지점과 종료 지점을 설정하여 다항식의 계수를 차선 변경 거리에 대한 함수로 해석하였다. 심층 강화 학습을 기반으로 계수들을 최적화하기 위하여, 강화 학습 아키텍처를 구성하였다. 관측 공간은 12가지의 주행 환경 데이터를 이용하였고, 강화 학습의 출력으로는 3차 함수의 변수인 차선 변경 거리를 선정하였다. 그리고 강화 학습의 학습 능력을 극대화할 수 있는 보상 공간을 설계하였다. 동적 보상, 정적 보상, 동적 벌칙, 정적 벌칙을 시뮬레이션의 매 단계마다 부여함으로써 보상 총 합이 최대화될 수 있는 방향으로 학습이 진행되었다. 최적화를 위한 알고리즘으로는 Deep Deterministic Policy Gradient agent가 사용되었다. 강화학습 아키텍처와 함께 동역학 시뮬레이션 프로그램에서의 차량 구동을 위한 알고리즘을 개발하였다. 먼저 응급상황시에 차량의 차선을 언제, 어떤 속도로, 어떤 방향으로 변경할 지 결정하는 의사결정 알고리즘을 개발하였다. 타이어와 도로 사이의 최대 마찰계수를 실시간으로 추정하여 주행 차량이 정지하기 위한 최소 거리를 산출함으로써 선행 차량과의 충돌 위험을 판단하였다. 또한 Gipps의 안전거리 공식을 사용하여 변경하고자 하는 차선에서 오는 차량과의 충돌 가능성을 감지하여 그 차량을 추월해서 앞으로 지나갈지, 추월을 당해서 뒤로 갈 것인지를 결정하는 알고리즘을 개발하였다. 이를 바탕으로 좌측 차선과 우측 차선의 충돌 위험성 및 안정성을 판단하여 최종적인 차선 변경을 위한 의사결정 알고리즘을 개발하였다. 구성된 강화 학습 구조를 통한 긴급 차선 변경 궤적과 차선 유지 장치, 적응형 순항 제어와 같은 일반 주행시의 궤적을 상황에 맞추어 출력하는 알고리즘을 개발하고 적응형 모델 예측 제어기를 통해 주행 차량을 구동하는 통합 알고리즘을 개발하였다. 본 연구의 마지막 단계로서, 개발된 긴급 차선 변경 경로 생성 알고리즘의 최적화를 위하여 심층 강화 학습이 수행되었다. 총 60,000회의 시행 착오 방식의 학습을 통해 각 주행 상황 별 최적의 차선 변경 제어 알고리즘을 개발하였고, 각 주행상황 별 최적의 차선 변경 궤적을 제시하였다.Chapter 1. Introduction 1 1.1. Research Background 1 1.2. Previous Research 5 1.3. Research Objective 9 1.4. Dissertation Overview 13 Chapter 2. Simulation Environment 19 2.1. Simulator 19 2.2. Scenario 26 Chapter 3. Methodology 28 3.1. Reinforcement learning 28 3.2. Deep reinforcement learning 30 3.3. Neural network 33 Chapter 4. DRL-enhanced Lane Change 36 4.1. Necessity of Evasive Steering Trajectory Optimization 36 4.2. Trajectory Planning 39 4.3. DRL Structure 42 4.3.1. Observation 43 4.3.2. Action 47 4.3.3. Reward 49 4.3.4. Neural Network Architecture 58 4.3.5. Deep Deterministic Policy Gradient (DDPG) Agent 60 Chapter 5. Autonomous Driving Algorithm Integration 64 5.1. Lane Change Decision Making 65 5.1.1. Longitudinal Collision Detection 66 5.1.2. Lateral Collision Detection 71 5.1.3. Lane Change Direction Decision 74 5.2. Path Planning 75 5.3. Vehicle Controller 76 5.4. Algorithm Integration 77 Chapter 6. Training & Results 79 Chapter 7. Conclusion 91 References 97 국문초록 104박

Multi-level decision framework collision avoidance algorithm in emergency scenarios

With the rapid development of autonomous driving, the attention of academia has increasingly focused on the development of anti-collision systems in emergency scenarios, which have a crucial impact on driving safety. While numerous anti-collision strategies have emerged in recent years, most of them only consider steering or braking. The dynamic and complex nature of the driving environment presents a challenge to developing robust collision avoidance algorithms in emergency scenarios. To address the complex, dynamic obstacle scene and improve lateral maneuverability, this paper establishes a multi-level decision-making obstacle avoidance framework that employs the safe distance model and integrates emergency steering and emergency braking to complete the obstacle avoidance process. This approach helps avoid the high-risk situation of vehicle instability that can result from the separation of steering and braking actions. In the emergency steering algorithm, we define the collision hazard moment and propose a multi-constraint dynamic collision avoidance planning method that considers the driving area. Simulation results demonstrate that the decision-making collision avoidance logic can be applied to dynamic collision avoidance scenarios in complex traffic situations, effectively completing the obstacle avoidance task in emergency scenarios and improving the safety of autonomous driving

Automated longitudinal control based on nonlinear recursive B-spline approximation for battery electric vehicles

This works presents a driver assistance system for energy-efficient ALC of a BEV. The ALC calculates a temporal velocity trajectory from map data. The trajectory is represented by a cubic B-spline function and results from an optimization problem with respect to travel time, driving comfort and energy consumption. For the energetic optimization we propose an adaptive model of the required electrical traction power. The simple power train of a BEV allows the formulation of constraints as soft constraints. This leads to an unconstrained optimization problem that can be solved with iterative filter-based data approximation algorithms. The result is a direct trajectory optimization method of which the effort grows linearly with the trajectory length, as opposed to exponentially as with most other direct methods. We evaluate ALC in real test drives with a BEV. We also investigate the energy-saving potential in driving simulations with ALC compared to MLC. On the chosen reference route the ALC saves up to 3.4% energy compared to MLC at same average velocity, and achieves a 2.6% higher average velocity than MLC at the same energy consumptio