Car Vision- Lane detection and Following .pdf

During the past 17 years, technology has progressed at astronomical speeds. We have experienced not only the birth of many new technologies but also the miniaturization of both technologies new and old. It would have been audacious in the beginning of the new millennium to expect the amount of technological development. Perhaps the next biggest change to our lives is going to be self-driving or autonomous vehicles. Personally, my motivation was grounded in the fact that I received an opportunity to learn a tremendous amount of knowledge in both a niche, modern, highlytechnological field and gained experience in the different facets of software development such as software architecture, design and testing. Additionally, having full autonomy over the decision process and design choices added a great sense of responsibility. This thesis deals with lane detection and driving of such autonomous vehicles. The goal of this thesis is to program an existing autonomous car, built using a Raspberry Pi 2, an Arduino Mega and 4 DC motors, to successfully navigate a black and white mat which is supposed to function as a model of a racetrack. A small camera is mounted on top of the car and this is used in conjunction with different computer vision techniques to analyse data and correctly predict the course the car should navigate.

Serial2.0.zip

Connect your Mac to just about anything. Connect to routers, servers, firewalls, industrial control and IoT devices with ease. Telnet and Raw Socket Support NEW Serial now includes comprehensive support for Telnet, including the RFC2217 extension for remote serial port control supported by many serial device servers. Serial supports raw TCP sockets as well. Flawless Emulation Serial is a full-featured terminal emulator supporting Xterm, VT102, and ANSI terminal controls. This allows you to navigate the menu-driven interfaces found in many routers, firewalls and switches and use text based programs including emacs, vi, and nano as if you were connected over the network. No Drivers Serial includes built-in, reliable support for almost every serial device on the market, sparing you the hassle of finding, installing, and updating drivers.

Lane Detection Algorithm for Intelligent Vehicles

Abstract: Lane detection is an important foundation in the development of intelligent vehicles. To address problems such as low detection accuracy of traditional methods and poor real-time performance of deep learning-based methodologies, a lane detection algorithm for intelligent vehicles in complex road conditions and dynamic environments was proposed. Firstly, converting the distorted image and using the superposition threshold algorithm for edge detection, an aerial view of the lane was obtained via region of interest extraction and inverse perspective transformation. Secondly, the random sample consensus algorithm was adopted to fit the curves of lane lines based on the third-order B-spline curve model, and fitting evaluation and curvature radius calculation were then carried out on the curve. Lastly, by using the road driving video under complex road conditions and the Tusimple dataset, simulation test experiments for lane detection algorithm were performed. The experimental results show that the average detection accuracy based on road driving video reached 98.49%, and the average processing time reached 21.5 ms. The average detection accuracy based on the Tusimple dataset reached 98.42%, and the average processing time reached 22.2 ms. Compared with traditional methods and deep learning-based methodologies, this lane detection algorithm had excellent accuracy and real-time performance, a high detection efficiency and a strong anti-interference ability. The accurate recognition rate and average processing time were significantly improved. The proposed algorithm is crucial in promoting the technological level of intelligent vehicle driving assistance and conducive to the further improvement of the driving safety of intelligent vehicles.

Learning Lightweight Lane Detection CNNs by Self Attention Distillation.pdf

Training deep models for lane detection is challenging due to the very subtle and sparse supervisory signals in- herent in lane annotations. Without learning from much richer context, these models often fail in challenging sce- narios, e.g., severe occlusion, ambiguous lanes, and poor lighting conditions. In this paper, we present a novel knowl- edge distillation approach, i.e., Self Attention Distillation (SAD), which allows a model to learn from itself and gains substantial improvement without any additional supervision or labels. Specifically, we observe that attention maps ex- tracted from a model trained to a reasonable level would encode rich contextual information. The valuable contex- tual information can be used as a form of ‘free’ supervision for further representation learning through performing top- down and layer-wise attention distillation within the net- work itself. SAD can be easily incorporated in any feed- forward convolutional neural networks (CNN) and does not increase the inference time. We validate SAD on three pop- ular lane detection benchmarks (TuSimple, CULane and BDD100K) using lightweight models such as ENet, ResNet- 18 and ResNet-34. The lightest model, ENet-SAD, per- forms comparatively or even surpasses existing algorithms. Notably, ENet-SAD has 20 × fewer parameters and runs 10 × faster compared to the state-of-the-art SCNN [16], while still achieving compelling performance in all bench- marks. Our code is available at https://github. com/cardwing/Codes-for-Lane-Detection.

3D-LaneNet- End-to-End 3D Multiple Lane Detection.pdf

We introduce a network that directly predicts the 3D layout of lanes in a road scene from a single image. This work marks a first attempt to address this task with onboard sensing without assuming a known constant lane width or relying on pre-mapped environments. Our network architecture, 3D-LaneNet, applies two new concepts: intranetwork inverse-perspective mapping (IPM) and anchorbased lane representation. The intra-network IPM projection facilitates a dual-representation information flow in both regular image-view and top-view. An anchor-percolumn output representation enables our end-to-end approach which replaces common heuristics such as clustering and outlier rejection, casting lane estimation as an object detection problem. In addition, our approach explicitly handles complex situations such as lane merges and splits. Results are shown on two new 3D lane datasets, a synthetic and a real one. For comparison with existing methods, we test our approach on the image-only tuSimple lane detection benchmark, achieving performance competitive with stateof-the-art

Design_and_Implementation_of_a_Wireless_OBD_II_Fle.pdf

Abstract—This paper describes the work that has been done in the design and development of a wireless OBD II fleet management system. The system aims to measure speed, distance and fuel consumption of vehicles for tracking and analysis purposes. An OBD II reader is designed to measure speed and mass air flow, from which distance and fuel consumption are also computed. This data is then transmitted via WiFi to a remote server. The system also implements GPS tracking to determine the location of the vehicle. A database management system is implemented at the remote server for the storage and management of transmitted data and a graphical user interface (GUI) is developed for analysing the transmitted data . Various qualification tests are conducted to verify the functionality of the system. The results demonstrate that the system is capable of reading the various parameters, and can successfully process, transmit and display the readings.

基于J2534协议的诊断仪与ECU模拟器接口设计.pdf

摘要: 针对目前在汽车后市场故障诊断仪诊断内容开发领域出现的由于平台复杂而导致诊断内容开发工程师效率低下的问题，对 开发平台结构的组成、ECU 模拟器软件的设计、J2534 协议接口的实现等方面进行了研究，提出了把调试平台简化为一台电脑，将 J2534 诊断接口协议应用到诊断软件和 ECU 模拟器的通信中的思路，实现了离线故障诊断的仿真; 开发了 ECU 模拟器及其与诊断 仪的通信接口，模拟器采用双进程模型和诊断仪进行了通信，其与已开发完成的故障诊断软件 DiagAnalyzer 间通过 J2534 协议所规定 的 API 完成了通信。研究和测试结果表明: 该 ECU 模拟器具备可行性和便捷性，诊断内容开发工程师只要在数据配置文件中写入特 定的诊断数据，即可完成对特定车型诊断仪数据的快速开发; 此外其可以和其他遵循 J2534 协议的故障诊断软件进行匹配和通信。 关键词: J2534 协议; ECU 模拟器; ISO15765; ＲPC 通信; 故障诊断仪

ELF for the Arm Architecture

This specification provides the processor-specific definitions required by ELF [SCO-ELF10] for Arm based systems. The ELF specification is part of the larger System V ABI specification where it forms chapters 4 and 5. However, the specification can be used in isolation as a generic object and executable format. Platform Standards (page 14) of this document covers ELF related matters that are platform specific. Most of this material is related to the Base Platform ABI. Object Files (page 20) and Program Loading and Dynamic Linking (page 46) of this document are struc- tured to correspond to chapters 4 and 5 of the ELF specification. Specifically: • Object Files (page 20) covers object files and relocations • Program Loading and Dynamic Linking (page 46) covers program loading and dynamic linking. There are several drafts of the ELF specification on the SCO web site. This specification is based on the December 2003 draft, which was the most recent stable draft at the time this specification was developed.

USB OTG晶片設計

USB的優點在於低廉易建置，缺點USB採用主從式架構(Master/slave system)，所有週邊裝置都要靠單一主控端 (PC)。 USB-IF 2001年12月公佈USB 2.0 On-The-Go (OTG)補充版 新增雙重角色裝置(Dual-role Device)及 HNP (Host Negotiation Protocol)、SRP (Session Request Protocol)兩種協定，使USB可以任意替換主控端(Host)和週邊端(Peripheral)的角色

The Linux Scheduler- a Decade of Wasted Cores.pdf

Classical scheduling problems revolve around setting the length of the scheduling quantum to provide interactive re- sponsiveness while minimizing the context switch overhead, simultaneously catering to batch and interactive workloads in a single system, and efficiently managing scheduler run queues. By and large, by the year 2000, operating systems designers considered scheduling to be a solved problem; the Linus Torvalds quote is an accurate reflection of the general opinion at that time.