Mitigating blind spot collision utilizing ultrasonic gap perimeter sensor

Failure to identify the vehicle by the side of the vehicle or in other word as blind spot area, especially larger vehicles are one of the causes of the accident. For some drivers, the simple solution is to place an additional side mirror. However, it is not the best solution because this additional side mirrors do not provide an accurate picture of actual or estimated distance to the object or another vehicle. The objective of this project is to identify the causes of automobile collisions, notably the side collision impact causes by the blind spot, to develop a system that can detect the presence vehicles on the side and to develop a system that are affordable for normal car users. To achieve this objective, flow chart was designed to help write coding using Arduino 1.0.2 and design hardware. This system can detect the obstacle within range 2cm to 320cm from the edge of the project vehicle. Before this system developed, the survey was conducted to determine what the driver wants. After that, the design process is carried out. The input to this system is Ping ultrasonic sensor, LCD, LED, and siren for the output part. LCD and LED were displaying the distance from the vehicle and the siren will be switched on to warn the driver when have obstacle in the blind spot area. As a conclusion, the Mitigating Blind Spot Collision Utilizing Ultrasonic Gap Perimeter Sensor System has successfully completed. This system able to detect the presence of other vehicles on the side of the project vehicle, especially in the blind spot area and will alert the driver when the vehicle is nearby when the alarm system is operated. The efficiency of this system to detect objects in the blind spot area is 79.82%. Others, it will give the display value less than one second after obstacle exists in front of the sensor. This operating time is most important because if the system is slow, the main function of this system to detect the obstacle in the blind spot area is not achieved

What Am I Testing and Where? Comparing Testing Procedures based on Lightweight Requirements Annotations

[Context] The testing of software-intensive systems is performed in different test stages each having a large number of test cases. These test cases are commonly derived from requirements. Each test stages exhibits specific demands and constraints with respect to their degree of detail and what can be tested. Therefore, specific test suites are defined for each test stage. In this paper, the focus is on the domain of embedded systems, where, among others, typical test stages are Software- and Hardware-in-the-loop. [Objective] Monitoring and controlling which requirements are verified in which detail and in which test stage is a challenge for engineers. However, this information is necessary to assure a certain test coverage, to minimize redundant testing procedures, and to avoid inconsistencies between test stages. In addition, engineers are reluctant to state their requirements in terms of structured languages or models that would facilitate the relation of requirements to test executions. [Method] With our approach, we close the gap between requirements specifications and test executions. Previously, we have proposed a lightweight markup language for requirements which provides a set of annotations that can be applied to natural language requirements. The annotations are mapped to events and signals in test executions. As a result, meaningful insights from a set of test executions can be directly related to artifacts in the requirements specification. In this paper, we use the markup language to compare different test stages with one another. [Results] We annotate 443 natural language requirements of a driver assistance system with the means of our lightweight markup language. The annotations are then linked to 1300 test executions from a simulation environment and 53 test executions from test drives with human drivers. Based on the annotations, we are able to analyze how similar the test stages are and how well test stages and test cases are aligned with the requirements. Further, we highlight the general applicability of our approach through this extensive experimental evaluation. [Conclusion] With our approach, the results of several test levels are linked to the requirements and enable the evaluation of complex test executions. By this means, practitioners can easily evaluate how well a systems performs with regards to its specification and, additionally, can reason about the expressiveness of the applied test stage.TU Berlin, Open-Access-Mittel - 202

A Study on Recent Developments and Issues with Obstacle Detection Systems for Automated Vehicles

This paper reviews current developments and discusses some critical issues with obstacle detection systems for automated vehicles. The concept of autonomous driving is the driver towards future mobility. Obstacle detection systems play a crucial role in implementing and deploying autonomous driving on our roads and city streets. The current review looks at technology and existing systems for obstacle detection. Specifically, we look at the performance of LIDAR, RADAR, vision cameras, ultrasonic sensors, and IR and review their capabilities and behaviour in a number of different situations: during daytime, at night, in extreme weather conditions, in urban areas, in the presence of smooths surfaces, in situations where emergency service vehicles need to be detected and recognised, and in situations where potholes need to be observed and measured. It is suggested that combining different technologies for obstacle detection gives a more accurate representation of the driving environment. In particular, when looking at technological solutions for obstacle detection in extreme weather conditions (rain, snow, fog), and in some specific situations in urban areas (shadows, reflections, potholes, insufficient illumination), although already quite advanced, the current developments appear to be not sophisticated enough to guarantee 100% precision and accuracy, hence further valiant effort is needed

An intra-vehicular wireless multimedia sensor network for smartphone-based low-cost advanced driver-assistance systems

Advanced driver-assistance system(s) (ADAS) are more prevalent in high-end vehicles than in low-end vehicles. Wired solutions of vision sensors in ADAS already exist, but are costly and do not cater for low-end vehicles. General ADAS use wired harnessing for communication; this approach eliminates the need for cable harnessing and, therefore, the practicality of a novel wireless ADAS solution was tested. A low-cost alternative is proposed that extends a smartphone’s sensor perception, using a camera-based wireless sensor network. This paper presents the design of a low-cost ADAS alternative that uses an intra-vehicle wireless sensor network structured by a Wi-Fi Direct topology, using a smartphone as the processing platform. The proposed system makes ADAS features accessible to cheaper vehicles and investigates the possibility of using a wireless network to communicate ADAS information in a intra-vehicle environment. Other ADAS smartphone approaches make use of a smartphone’s onboard sensors; however, this paper shows the application of essential ADAS features developed on the smartphone’s ADAS application, carrying out both lane detection and collision detection on a vehicle by using wireless sensor data. A smartphone’s processing power was harnessed and used as a generic object detector through a convolution neural network, using the sensory network’s video streams. The network’s performance was analysed to ensure that the network could carry out detection in real-time. A low-cost CMOS camera sensor network with a smartphone found an application, using Wi-Fi Direct, to create an intra-vehicle wireless network as a low-cost advanced driver-assistance system.DATA AVAILABLITY STATEMENT : Publicly available datasets were analysed in this study. There data can be found here: https://github.com/TuSimple/tusimple-benchmark and https://boxy-dataset.com/ boxy/ accessed on 25 November 2021.https://www.mdpi.com/journal/sensorsam2023Electrical, Electronic and Computer Engineerin

Object Detection in 20 Years: A Survey

Object detection, as of one the most fundamental and challenging problems in computer vision, has received great attention in recent years. Its development in the past two decades can be regarded as an epitome of computer vision history. If we think of today's object detection as a technical aesthetics under the power of deep learning, then turning back the clock 20 years we would witness the wisdom of cold weapon era. This paper extensively reviews 400+ papers of object detection in the light of its technical evolution, spanning over a quarter-century's time (from the 1990s to 2019). A number of topics have been covered in this paper, including the milestone detectors in history, detection datasets, metrics, fundamental building blocks of the detection system, speed up techniques, and the recent state of the art detection methods. This paper also reviews some important detection applications, such as pedestrian detection, face detection, text detection, etc, and makes an in-deep analysis of their challenges as well as technical improvements in recent years.Comment: This work has been submitted to the IEEE TPAMI for possible publicatio

교차로에서 자율주행 차량의 제한된 가시성과 불확실성을 고려한 종방향 거동계획

학위논문(박사) -- 서울대학교대학원 : 공과대학 기계공학부, 2023. 2. 이경수.This dissertation presents a novel longitudinal motion planning of autonomous vehicle at urban intersection to overcome the limited visibility due to complicated road structures and sensor specification, guaranteeing the safety from the potential collision with vehicles appearing from the occluded region. The intersection autonomous driving requires high level of safety due to congested traffics and environmental complexities. Due to complicated road structures and the detection range of perception sensors, the occluded region is generated in urban autonomous driving. The virtual target is one of the motion planning methods to react the sudden appearance of vehicles from the blind spot. The Gaussian Process Regression (GPR) is implemented to train the virtual target model to generate various future driving trajectories interacting with the motion of the ego vehicle. The GPR model provides not only the predicted trajectories of the virtual target but also the uncertainty of the future motion. Therefore, prediction results from GPR can be utilized to a position constraint for the Model Predictive Control (MPC), and the uncertainties are taken into account as a chance constraint in the MPC. In order to comprehend the surrounding environment including dynamic objects, a region of interest (ROI) is defined to determine targets of the interest. With the pre-determined driving route of the ego vehicle and the route information of the intersection, driving lanes intersecting with the ego driving lane can be determined, and the intersecting lanes are defined as ROI, reducing the computational load by eliminating targets of disinterest. Then the future motion of the selected target is predicted by a Long Short-Term Memory-Recurrent Neural Network (LSTM-RNN). Driving data for training are directly obtained with two different autonomous vehicles, providing their odometry information regardless to the limited field of view (FOV). For a widely known autonomous driving datasets such as Waymo and nuScenes, the vehicle odometry information are collected from the perceptive sensors mounted on the test vehicle. Thus, information of target that are out of the FOV of the test vehicle cant be obtained. The obtained training data are organized in the target centered coordinates for better input-domain adaptation and generalization. The mean squared error and the negative log likelihood loss functions are adapted to train and provide the uncertainty information of the target vehicle for the motion planning of the autonomous vehicle. The MPC with a chance constraint is formulated to optimize the longitudinal motion of the autonomous vehicle. The dynamic and actuator constraints are designed to provide ride comfort and safety to drivers. The position constraint with the chance constraint guarantees the safety and prevent the potential collision with target vehicles. The position constraint for the travel distance over the prediction horizon time is determined based on the clearance between the predicted trajectories of the target and ego vehicle at every prediction sample time. The performance and feasibility of the proposed algorithm are evaluated via computer simulation and test-data based simulation. The offline simulation validates the safety of the proposed algorithm, and the suggested motion planner has been implemented on an autonomous driving vehicle and tested in a real road. Through the implementation of the algorithm to an actual vehicle, the suggested algorithm is confirmed to be applicable in real life autonomous driving.본 논문은 복잡한 도로 구조와 센서 사양으로 인한 시야 제한을 극복하며 사각지대에서 등장하는 차량과의 잠재적인 충돌로부터 안전을 보장하기 위한 도심 교차로에서의 자율주행차의 새로운 종방향 거동 계획을 제시한다. 도심 자율주행은 교통체증과 환경의 복잡성으로 인해 높은 수준의 안전성이 요구됩니다. 복잡한 도로 구조와 인지 센서의 인지 범위로 인해 도심 자율주행에서는 사각지대가 발생한다. 가상 타겟은 사각지대에서 차량의 갑작스러운 출현에 대응하기 위한 거동 계획 방법 중 하나입니다. 자차량의 거동과 상호작용하는 다양한 미래 주행 궤적을 생성하는 가상 타겟 모델을 구현하기 위하여 Gaussian Process Regression (GPR) 방법을 사용합니다. GPR 모델은 가상 표적의 예측된 궤적뿐만 아니라 미래 궤적에 대한 불확실성도 제공합니다. 따라서 GPR의 예측 결과는 Model Predictive Control (MPC)에 대한 위치 제약 조건으로 활용될 수 있으며 불확실성은 MPC에서 기회 제약 조건으로 고려됩니다. 동적 객체를 포함한 주변 환경을 파악하기 위해 관심영역을 정의하여 목표 대상을 결정합니다. 미리 결정된 자차량의 주행경로와 교차로의 경로정보를 통하여 자차량의 주행차로와 교차하는 다른 차선을 판단하여 관심영역으로 정의함으로써 관심영역 밖의 차량을 제외하여 연산량을 감소시킬 수 있다. 다음으로 인지된 차량의 미래 이동 궤적은 LSTM-RNN (Long Short-Term Memory Recurrent Neural Network)에 의해 예측됩니다. 훈련을 위한 주행 데이터는 두 대의 자율주행 차량에서 직접 획득하여 제한된 시야에 관계없이 차량의 상태 정보를 제공합니다. 구글 Waymo 및 nuScenes와 같이 널리 알려진 자율주행 데이터의 경우 차량 상태 정보는 테스트 차량에 장착된 인지 센서에서 수집됩니다. 따라서 테스트 차량의 시야에서 벗어나 있는 차량 정보는 얻을 수 없습니다. 취득한 주행 데이터는 더 나은 입력 데이터 적응 및 일반화를 위해 자차가 아닌 타겟차량 중심 좌표로 구성됩니다. 손실함수로 평균 제곱 오차 및 음의 로그 우도함수를 사용하였고 음의 로그 우도함수는 자율주행 차량의 거동계획에 사용될 수 있게 타겟차량의 미래 궤적에 대한 불확실성 정보를 제공한다. 기회 제약 조건이 있는 MPC는 자율차량의 종방향 거동을 최적화하도록 구현됩니다. 동적 제약 조건 및 구동기 제약 조건은 운전자에게 승차감과 안전을 제공하도록 설계되었습니다. 기회 제약 조건은 위치 제약 조건을 강건하게 하여 안전을 보장하고 대상 차량과의 잠재적인 충돌을 방지합니다. 예측 시간동안 이동 거리에 대한 위치 제약 조건은 각 예측시간의 타겟과 자차량의 예측된 궤적 간의 거리 차이에 의해 결정된다. 제안한 알고리즘의 성능과 타당성은 컴퓨터 시뮬레이션과 테스트 데이터 기반 시뮬레이션을 통해 평가된다. 오프라인 시뮬레이션을 통해 제안한 알고리즘의 안전성을 검증하였으며 제안한 거동계획 알고리즘을 자율주행차에 구현하여 실제 도로에서 테스트하였다. 제안한 알고리즘을 실제 차량에 구현하여 실제 자율주행에 적용할 수 있음을 확인하였다.Chapter 1. Introduction 1 1.1. Research Background and Motivation of Intersection Autonomous Driving 1 1.2. Previous Researches on Intersection Autonomous Driving 9 1.2.1. Research on Trajectory Prediction and Intention Inference at Urban Intersection 10 1.2.2. Research on Intersection Motion Planning 11 1.3. Thesis Objectives 18 1.4. Thesis Outline 19 Chapter 2. Overall Architecture of Intersection Autonomous Driving System 22 2.1. Software Configuration of Intersection Autonomous Driving 22 2.2. Hardware Configuration of Autonomous Driving and Test Vehicle 24 2.3. Vehicle Test Environment for Intersection Autonomous Driving 25 Chapter 3. Virtual Target Modelling for Intersection Motion Planning 27 3.1. Limitation of Conventional Virtual Target Model for Intersection 27 3.2. Virtual Target Generation for Intersection Occlusion 31 3.3. Intersection Virtual Target Modeling 34 3.3.1. Gaussian Process Regression based Virtual Target Model at Intersection 35 3.3.2. Data Processing for Gaussian Process Regression based Virtual Target Model 38 3.3.3. Definition of Visibility Index of Virtual Target at Intersection 45 3.3.4. Long Short-Term Memory based Virtual Target Model at Intersection 51 Chapter 4. Surrounding Vehicle Motion Prediction at Intersection 54 4.1. Intersection Surrounding Vehicle Classification 54 4.2. Data-driven Vehicle State based Motion Prediction at Intersection 58 4.2.1. Network Architecture of Motion Predictor 58 4.2.2. Dataset Processing of the Network 65 Chapter 5. Intersection Longitudinal Motion Planning 68 5.1. Outlines of Longitudinal Motion Planning with Model Predictive Control 68 5.2. Stochastic Model Predictive Control of Intersection Motion Planner 69 5.2.1. Definition of System Dynamics Model 69 5.2.2. Ego Vehicle Prediction and Reference States Definition 70 5.2.3. Safety Clearance Decision for Intersection Collision Avoidance 71 5.2.4. Driving Mode Decision of Intersection Motion Planning 79 5.2.5. Formulation of Model Predictive Control with the Chance Constraint 83 Chapter 6. Performance Evaluation of Intersection Longitudinal Motion Planning 86 6.1. Performance Evaluation of Virtual Target Prediction at Intersection 86 6.1.1. GPR based Virtual Target Model Prediction Results 86 6.1.2. Intersection Autonomous Driving Computer Simulation Environment 90 6.1.2.1. Simulation Result of Effect of Virtual Target in Intersection Autonomous Driving 92 6.1.2.2. Virtual Target Simulation Result of the Right Turn Across Path Scenario in the Intersection 96 6.1.2.3. Virtual Target Simulation Result of the Straight Across Path Scenario in the Intersection 102 6.1.2.4. Virtual Target Simulation Result of the Left Turn Across Path Scenario in the Intersection 108 6.1.2.5. Virtual Target Simulation Result of Crooked T-shaped Intersection 113 6.2. Performance Evaluation of Data-driven Vehicle State based Motion Prediction at Intersection 124 6.2.1. Data-driven Motion Prediction Accuracy Analysis 124 6.2.2. Prediction Trajectory Accuracy Analysis 134 6.3. Vehicle Test for Intersection Autonomous Driving 146 6.3.1. Test Vehicle Configuration for Intersection Autonomous Driving 146 6.3.2. Software Configuration for Autonomous Vehicle Operation 147 6.3.3. Vehicle Test Environment for Intersection Autonomous Driving 148 6.3.4. Vehicle Test Result of Intersection Autonomous Driving 151 Chapter 7. Conclusion and Future Work 161 7.1. Conclusion 161 7.2. Future Work 164 Bibliography 166 Abstract in Korean 172박

An intra-vehicular wireless multimedia sensor network for smartphone-based low-cost advanced driver-assistance systems

Advanced driver-assistance systems (ADAS) are more prevalent in high-end vehicles than in low-end vehicles. The research proposes an alternative for drivers without having to wait years to gain access to the safety ADAS offers. Wireless Multimedia Sensor Networks (WMSN) for ADAS applications in collaboration with smartphones is non-existent. Intra-vehicle environments cause difficulties in data transfer for wireless networks where performance of such networks in an intra-vehicle network is investigated. A low-cost alternative was proposed that extends a smartphone’s sensor perception, using a camera- based wireless sensor network. This dissertation presents the design of a low-cost ADAS alternative that uses an intra-vehicle wireless sensor network structured by a Wi-Fi Direct topology, using a smartphone as the processing platform. In addition, to expand on the smartphone’s other commonly available wireless protocols, the Bluetooth protocol was used to collect blind spot sensory data, being processed by the smartphone. Both protocols form part of the Intra-Vehicular Wireless Sensor Network (IVWSN). Essential ADAS features developed on the smartphone ADAS application carried out both lane detection and collision detection on a vehicle. A smartphone’s processing power was harnessed and used as a generic object detector through a convolution neural network, using the sensory network’s video streams. Blind spot sensors on the lateral sides of the vehicle provided sensory data transmitted to the smartphone through Bluetooth. IVWSNs are complex environments with many reflective materials that may impede communication. The network in a vehicle environment should be reliable. The network’s performance was analysed to ensure that the network could carry out detection in real-time, which would be essential for the driver’s safety. General ADAS systems use wired harnessing for communication and, therefore, the practicality of a novel wireless ADAS solution was tested. It was found that a low-cost advanced driver-assistance system alternative can be conceptualised by using object detection techniques being processed on a smartphone from multiple streams, sourced from an IVWSN, composed of camera sensors. A low-cost CMOS camera sensors network with a smartphone found an application, using Wi-Fi Direct to create an intra-vehicle wireless network as a low-cost advanced driver-assistance system.Dissertation (MEng (Computer Engineering))--University of Pretoria, 2021.Electrical, Electronic and Computer EngineeringMEng (Computer Engineering)Unrestricte

Evaluation of surface defect detection in reinforced concrete bridge decks using terrestrial LiDAR

Routine bridge inspections require labor intensive and highly subjective visual interpretation to determine bridge deck surface condition. Light Detection and Ranging (LiDAR) a relatively new class of survey instrument has become a popular and increasingly used technology for providing as-built and inventory data in civil applications. While an increasing number of private and governmental agencies possess terrestrial and mobile LiDAR systems, an understanding of the technology’s capabilities and potential applications continues to evolve. LiDAR is a line-of-sight instrument and as such, care must be taken when establishing scan locations and resolution to allow the capture of data at an adequate resolution for defining features that contribute to the analysis of bridge deck surface condition. Information such as the location, area, and volume of spalling on deck surfaces, undersides, and support columns can be derived from properly collected LiDAR point clouds. The LiDAR point clouds contain information that can provide quantitative surface condition information, resulting in more accurate structural health monitoring. LiDAR scans were collected at three study bridges, each of which displayed a varying degree of degradation. A variety of commercially available analysis tools and an independently developed algorithm written in ArcGIS Python (ArcPy) were used to locate and quantify surface defects such as location, volume, and area of spalls. The results were visual and numerically displayed in a user-friendly web-based decision support tool integrating prior bridge condition metrics for comparison. LiDAR data processing procedures along with strengths and limitations of point clouds for defining features useful for assessing bridge deck condition are discussed. Point cloud density and incidence angle are two attributes that must be managed carefully to ensure data collected are of high quality and useful for bridge condition evaluation. When collected properly to ensure effective evaluation of bridge surface condition, LiDAR data can be analyzed to provide a useful data set from which to derive bridge deck condition information