Key-point based tracking for illegally parked vehicle detection

This research aims to develop a target detection and tracking system that can realize real-time video surveillance. The purpose of the research is to realize a monitoring application that can run automatically and intelligently to detect and track illegally parked vehicles. Since the application scenario of the algorithm is a real traffic environment, it must be able to adapt to complex environmental interference, such as drastic changes in lighting conditions, frequent occlusion, and long-term stable tracking. The thesis shows the detailed design process and test results of the system. This algorithm combines the target detection function based on deep learning network and the multi-object tracking algorithm based on key point matching. The method shown in the thesis focuses on detecting and tracking stationary vehicles in the no parking area. An object detection algorithm based on a deep learning network is used to recognize vehicles. Once the recognized vehicle is defined as an illegally parked vehicle through the determination of its motion state and location, an algorithm based on key-point matching is developed and tracked for this type of vehicle. If the target is still stationary in the no parking area after a period, the system will generate an alarm. The method was tested in more than 20 hours of video. The video comes from public database and our own. They all show real surveillance scenes, including different time periods of the day and different locations. The test results show that the method achieves 100% in precision (also called positive predictive value), 95% in recall (also known as sensitivity) and 97% in F1 (a measure that combines precision and recall). The results obtained also produce better detection and tracking compared to other comparable methods

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

Multimodal machine learning for intelligent mobility

Scientific problems are solved by finding the optimal solution for a specific task. Some problems can be solved analytically while other problems are solved using data driven methods. The use of digital technologies to improve the transportation of people and goods, which is referred to as intelligent mobility, is one of the principal beneficiaries of data driven solutions. Autonomous vehicles are at the heart of the developments that propel Intelligent Mobility. Due to the high dimensionality and complexities involved in real-world environments, it needs to become commonplace for intelligent mobility to use data-driven solutions. As it is near impossible to program decision making logic for every eventuality manually. While recent developments of data-driven solutions such as deep learning facilitate machines to learn effectively from large datasets, the application of techniques within safety-critical systems such as driverless cars remain scarce.Autonomous vehicles need to be able to make context-driven decisions autonomously in different environments in which they operate. The recent literature on driverless vehicle research is heavily focused only on road or highway environments but have discounted pedestrianized areas and indoor environments. These unstructured environments tend to have more clutter and change rapidly over time. Therefore, for intelligent mobility to make a significant impact on human life, it is vital to extend the application beyond the structured environments. To further advance intelligent mobility, researchers need to take cues from multiple sensor streams, and multiple machine learning algorithms so that decisions can be robust and reliable. Only then will machines indeed be able to operate in unstructured and dynamic environments safely. Towards addressing these limitations, this thesis investigates data driven solutions towards crucial building blocks in intelligent mobility. Specifically, the thesis investigates multimodal sensor data fusion, machine learning, multimodal deep representation learning and its application of intelligent mobility. This work demonstrates that mobile robots can use multimodal machine learning to derive driver policy and therefore make autonomous decisions.To facilitate autonomous decisions necessary to derive safe driving algorithms, we present an algorithm for free space detection and human activity recognition. Driving these decision-making algorithms are specific datasets collected throughout this study. They include the Loughborough London Autonomous Vehicle dataset, and the Loughborough London Human Activity Recognition dataset. The datasets were collected using an autonomous platform design and developed in house as part of this research activity. The proposed framework for Free-Space Detection is based on an active learning paradigm that leverages the relative uncertainty of multimodal sensor data streams (ultrasound and camera). It utilizes an online learning methodology to continuously update the learnt model whenever the vehicle experiences new environments. The proposed Free Space Detection algorithm enables an autonomous vehicle to self-learn, evolve and adapt to new environments never encountered before. The results illustrate that online learning mechanism is superior to one-off training of deep neural networks that require large datasets to generalize to unfamiliar surroundings. The thesis takes the view that human should be at the centre of any technological development related to artificial intelligence. It is imperative within the spectrum of intelligent mobility where an autonomous vehicle should be aware of what humans are doing in its vicinity. Towards improving the robustness of human activity recognition, this thesis proposes a novel algorithm that classifies point-cloud data originated from Light Detection and Ranging sensors. The proposed algorithm leverages multimodality by using the camera data to identify humans and segment the region of interest in point cloud data. The corresponding 3-dimensional data was converted to a Fisher Vector Representation before being classified by a deep Convolutional Neural Network. The proposed algorithm classifies the indoor activities performed by a human subject with an average precision of 90.3%. When compared to an alternative point cloud classifier, PointNet[1], [2], the proposed framework out preformed on all classes. The developed autonomous testbed for data collection and algorithm validation, as well as the multimodal data-driven solutions for driverless cars, is the major contributions of this thesis. It is anticipated that these results and the testbed will have significant implications on the future of intelligent mobility by amplifying the developments of intelligent driverless vehicles.</div

A deep reinforcement learning based homeostatic system for unmanned position control

Deep Reinforcement Learning (DRL) has been proven to be capable of designing an optimal control theory by minimising the error in dynamic systems. However, in many of the real-world operations, the exact behaviour of the environment is unknown. In such environments, random changes cause the system to reach different states for the same action. Hence, application of DRL for unpredictable environments is difficult as the states of the world cannot be known for non-stationary transition and reward functions. In this paper, a mechanism to encapsulate the randomness of the environment is suggested using a novel bio-inspired homeostatic approach based on a hybrid of Receptor Density Algorithm (an artificial immune system based anomaly detection application) and a Plastic Spiking Neuronal model. DRL is then introduced to run in conjunction with the above hybrid model. The system is tested on a vehicle to autonomously re-position in an unpredictable environment. Our results show that the DRL based process control raised the accuracy of the hybrid model by 32%.N/

Ego-motion and Surrounding Vehicle State Estimation Using a Monocular Camera

Understanding ego-motion and surrounding vehicle state is essential to enable automated driving and advanced driving assistance technologies. Typical approaches to solve this problem use fusion of multiple sensors such as LiDAR, camera, and radar to recognize surrounding vehicle state, including position, velocity, and orientation. Such sensing modalities are overly complex and costly for production of personal use vehicles. In this paper, we propose a novel machine learning method to estimate ego-motion and surrounding vehicle state using a single monocular camera. Our approach is based on a combination of three deep neural networks to estimate the 3D vehicle bounding box, depth, and optical flow from a sequence of images. The main contribution of this paper is a new framework and algorithm that integrates these three networks in order to estimate the ego-motion and surrounding vehicle state. To realize more accurate 3D position estimation, we address ground plane correction in real-time. The efficacy of the proposed method is demonstrated through experimental evaluations that compare our results to ground truth data available from other sensors including Can-Bus and LiDAR

Joint-SRVDNet: Joint Super Resolution and Vehicle Detection Network

In many domestic and military applications, aerial vehicle detection and super-resolutionalgorithms are frequently developed and applied independently. However, aerial vehicle detection on super-resolved images remains a challenging task due to the lack of discriminative information in the super-resolved images. To address this problem, we propose a Joint Super-Resolution and Vehicle DetectionNetwork (Joint-SRVDNet) that tries to generate discriminative, high-resolution images of vehicles fromlow-resolution aerial images. First, aerial images are up-scaled by a factor of 4x using a Multi-scaleGenerative Adversarial Network (MsGAN), which has multiple intermediate outputs with increasingresolutions. Second, a detector is trained on super-resolved images that are upscaled by factor 4x usingMsGAN architecture and finally, the detection loss is minimized jointly with the super-resolution loss toencourage the target detector to be sensitive to the subsequent super-resolution training. The network jointlylearns hierarchical and discriminative features of targets and produces optimal super-resolution results. Weperform both quantitative and qualitative evaluation of our proposed network on VEDAI, xView and DOTAdatasets. The experimental results show that our proposed framework achieves better visual quality than thestate-of-the-art methods for aerial super-resolution with 4x up-scaling factor and improves the accuracy ofaerial vehicle detection

Detection of Lying Electrical Vehicles in Charging Coordination Application Using Deep Learning

The simultaneous charging of many electric vehicles (EVs) stresses the distribution system and may cause grid instability in severe cases. The best way to avoid this problem is by charging coordination. The idea is that the EVs should report data (such as state-of-charge (SoC) of the battery) to run a mechanism to prioritize the charging requests and select the EVs that should charge during this time slot and defer other requests to future time slots. However, EVs may lie and send false data to receive high charging priority illegally. In this paper, we first study this attack to evaluate the gains of the lying EVs and how their behavior impacts the honest EVs and the performance of charging coordination mechanism. Our evaluations indicate that lying EVs have a greater chance to get charged comparing to honest EVs and they degrade the performance of the charging coordination mechanism. Then, an anomaly based detector that is using deep neural networks (DNN) is devised to identify the lying EVs. To do that, we first create an honest dataset for charging coordination application using real driving traces and information revealed by EV manufacturers, and then we also propose a number of attacks to create malicious data. We trained and evaluated two models, which are the multi-layer perceptron (MLP) and the gated recurrent unit (GRU) using this dataset and the GRU detector gives better results. Our evaluations indicate that our detector can detect lying EVs with high accuracy and low false positive rate