Presentation of simultaneous localization and mapping problem in the framework of self-driving. Autonomous Driving Lab project is carried out by University of Tartu in collaboration with Bolt company.
Using amazon machine learning to identify trends in io t data technical 201Amazon Web Services
Internet of Things is creating a tidal wave of new data including events, correlations, business value, and much more. With the proliferation of new data sets, it also introduces more potential issues, errors, and spurious values.
In this session, we will explore using Amazon Machine Learning to analyse and understand the new data collected within your IoT solution. In addition, we will learn how to discover patterns, trends, anomalies, and correlations by demonstrating the capabilities of Amazon Machine Learning and SparkML running on AWS Cloud.
Speaker: Simon Elisha, Solutions Architect, Amazon Web Services
The document summarizes the architectures of three self-driving vehicles that competed in the 2007 DARPA Urban Challenge: Talos (MIT), Boss (CMU), and Junior (Stanford). All three vehicles used similar sensing technologies like LiDAR and radar for perception tasks like obstacle detection and tracking. They also had components for localization, mapping, planning paths and behaviors. Talos stood out for its unified planning and control system, Boss for its behavioral executive, and Junior for its precise localization. The Challenge marked early progress in autonomous driving and showcased different technical approaches to navigation in urban environments.
Driverless vehicles, also known as autonomous vehicles, can transport passengers from one destination to another without human involvement. They use sensors like lidar and radar along with GPS and digital maps to navigate roads automatically. Some key technologies that enable autonomous driving include adaptive cruise control, automatic emergency braking, and self-parking. Lidar systems are important for driverless cars as they use lasers to generate 3D images of the vehicle's surroundings up to 200 meters away. While driverless vehicles could improve safety and mobility, challenges remain regarding their high costs, ability to perceive environments, need for infrastructure upgrades, and ensuring they function properly in all conditions.
This document discusses the working of autonomous vehicles. It describes how autonomous vehicles use various sensors like radar, lidar, ultrasonic sensors, wheel speed sensors, GPS, and cameras to perceive their surroundings and navigate without human input. It also discusses the processors used to make sense of the large amounts of data collected by the sensors and control the vehicle. The sensors work together to build a 3D map of the vehicle's environment to allow it to detect objects and obstacles and safely drive itself.
Computer Vision for Advanced Driver Assistance Systems (Olga Mirkina Technolo...IT Arena
Lviv IT Arena is a conference specially designed for programmers, designers, developers, top managers, inverstors, entrepreneurs and startuppers. Annually it takes place at the beginning of October in Lviv at Arena Lviv stadium. In 2016 the conference gathered more than 1800 participants and over 100 speakers from companies like Microsoft, Philips, Twitter, UBER and IBM. More details about the conference at itarena.lviv.ua.
This document describes the components and working of an autonomous vehicle system. It lists the major components as LADAR, RADAR, ultrasonic sensors, cameras, wheel encoders, GPS system, and a control unit. It provides details on how each component works, such as how LADAR uses lasers to measure distance and RADAR uses radio waves. The control unit receives input from all components to send commands to the vehicle and navigate safely. Potential advantages are fewer accidents and saved time, while disadvantages include higher costs and need for advanced GPS.
Google self-driving car is any in a range of autonomous cars, developed by Google X as part of its project to develop technology for mainly electric cars. The software installed in Google's cars is called Google Chauffeur.[1] Lettering on the side of each car identifies it as a "self-driving car". The project was formerly led by Sebastian Thrun, former director of the Stanford Artificial Intelligence Laboratory and co-inventor of Google Street View. Thrun's team at Stanford created the robotic vehicle Stanley which won the 2005 DARPA Grand Challenge and its US$2 million prize from the United States Department of Defense.[2] The team developing the system consisted of 15 engineers working for Google, including Chris Urmson, Mike Montemerlo, and Anthony Levandowski who had worked on the DARPA Grand and Urban Challenges.
Legislation has been passed in four U.S. states and Washington, D.C. allowing driverless cars. The state of Nevada passed a law on June 29, 2011, permitting the operation of autonomous cars in Nevada, after Google had been lobbying in that state for robotic car laws.The Nevada law went into effect on March 1, 2012, and the Nevada Department of Motor Vehicles issued the first license for an autonomous car in May 2012, to a Toyota Prius modified with Google's experimental driverless technology. In April 2012, Florida became the second state to allow the testing of autonomous cars on public roads,and California became the third when Governor Jerry Brown signed the bill into law at Google HQ in Mountain View.[8] In December 2013, Michigan became the fourth state to allow testing of driverless cars on public roads.In July 2014, the city of Coeur d'Alene, Idaho adopted a robotics ordinance that includes provisions to allow for self-driving cars.
Using amazon machine learning to identify trends in io t data technical 201Amazon Web Services
Internet of Things is creating a tidal wave of new data including events, correlations, business value, and much more. With the proliferation of new data sets, it also introduces more potential issues, errors, and spurious values.
In this session, we will explore using Amazon Machine Learning to analyse and understand the new data collected within your IoT solution. In addition, we will learn how to discover patterns, trends, anomalies, and correlations by demonstrating the capabilities of Amazon Machine Learning and SparkML running on AWS Cloud.
Speaker: Simon Elisha, Solutions Architect, Amazon Web Services
The document summarizes the architectures of three self-driving vehicles that competed in the 2007 DARPA Urban Challenge: Talos (MIT), Boss (CMU), and Junior (Stanford). All three vehicles used similar sensing technologies like LiDAR and radar for perception tasks like obstacle detection and tracking. They also had components for localization, mapping, planning paths and behaviors. Talos stood out for its unified planning and control system, Boss for its behavioral executive, and Junior for its precise localization. The Challenge marked early progress in autonomous driving and showcased different technical approaches to navigation in urban environments.
Driverless vehicles, also known as autonomous vehicles, can transport passengers from one destination to another without human involvement. They use sensors like lidar and radar along with GPS and digital maps to navigate roads automatically. Some key technologies that enable autonomous driving include adaptive cruise control, automatic emergency braking, and self-parking. Lidar systems are important for driverless cars as they use lasers to generate 3D images of the vehicle's surroundings up to 200 meters away. While driverless vehicles could improve safety and mobility, challenges remain regarding their high costs, ability to perceive environments, need for infrastructure upgrades, and ensuring they function properly in all conditions.
This document discusses the working of autonomous vehicles. It describes how autonomous vehicles use various sensors like radar, lidar, ultrasonic sensors, wheel speed sensors, GPS, and cameras to perceive their surroundings and navigate without human input. It also discusses the processors used to make sense of the large amounts of data collected by the sensors and control the vehicle. The sensors work together to build a 3D map of the vehicle's environment to allow it to detect objects and obstacles and safely drive itself.
Computer Vision for Advanced Driver Assistance Systems (Olga Mirkina Technolo...IT Arena
Lviv IT Arena is a conference specially designed for programmers, designers, developers, top managers, inverstors, entrepreneurs and startuppers. Annually it takes place at the beginning of October in Lviv at Arena Lviv stadium. In 2016 the conference gathered more than 1800 participants and over 100 speakers from companies like Microsoft, Philips, Twitter, UBER and IBM. More details about the conference at itarena.lviv.ua.
This document describes the components and working of an autonomous vehicle system. It lists the major components as LADAR, RADAR, ultrasonic sensors, cameras, wheel encoders, GPS system, and a control unit. It provides details on how each component works, such as how LADAR uses lasers to measure distance and RADAR uses radio waves. The control unit receives input from all components to send commands to the vehicle and navigate safely. Potential advantages are fewer accidents and saved time, while disadvantages include higher costs and need for advanced GPS.
Google self-driving car is any in a range of autonomous cars, developed by Google X as part of its project to develop technology for mainly electric cars. The software installed in Google's cars is called Google Chauffeur.[1] Lettering on the side of each car identifies it as a "self-driving car". The project was formerly led by Sebastian Thrun, former director of the Stanford Artificial Intelligence Laboratory and co-inventor of Google Street View. Thrun's team at Stanford created the robotic vehicle Stanley which won the 2005 DARPA Grand Challenge and its US$2 million prize from the United States Department of Defense.[2] The team developing the system consisted of 15 engineers working for Google, including Chris Urmson, Mike Montemerlo, and Anthony Levandowski who had worked on the DARPA Grand and Urban Challenges.
Legislation has been passed in four U.S. states and Washington, D.C. allowing driverless cars. The state of Nevada passed a law on June 29, 2011, permitting the operation of autonomous cars in Nevada, after Google had been lobbying in that state for robotic car laws.The Nevada law went into effect on March 1, 2012, and the Nevada Department of Motor Vehicles issued the first license for an autonomous car in May 2012, to a Toyota Prius modified with Google's experimental driverless technology. In April 2012, Florida became the second state to allow the testing of autonomous cars on public roads,and California became the third when Governor Jerry Brown signed the bill into law at Google HQ in Mountain View.[8] In December 2013, Michigan became the fourth state to allow testing of driverless cars on public roads.In July 2014, the city of Coeur d'Alene, Idaho adopted a robotics ordinance that includes provisions to allow for self-driving cars.
The document provides an introduction to self-driving cars from Dr. Punnu Phairatt, a self-driving car engineer. It begins with an overview of the different levels of autonomy in self-driving cars from fully manual to fully autonomous without human intervention. It then discusses the key components that enable self-driving functionality, including sensors, localization, perception, decision making, navigation, and control. The rest of the document includes details on challenges in autonomous driving, different software approaches, and a proposed system design architecture with examples of localization, decision making, and navigation modules. It concludes with a quick demo of self-driving software and a discussion on further developing open source self-driving car software.
This document discusses automated or driverless cars. It describes how driverless cars use sensors like LIDAR and radar along with artificial intelligence, GPS, and Google Maps to navigate without human intervention. The car's AI software is connected to all sensors and controls systems like steering and brakes based on input from sensors and maps. Major companies developing driverless car technology include Google, GM, Ford, Audi, BMW, Volkswagen and Volvo. Benefits include eliminating accidents from human error, improving traffic flow, and allowing passengers to work or rest while the car drives itself.
The document provides an overview of driverless or autonomous vehicles. It discusses the history and components of these vehicles, including sensors like LIDAR, radar and cameras. The document explains how artificial intelligence analyzes sensor data to navigate autonomously. Potential advantages are reduced accidents and increased road capacity, while obstacles include handling various weather conditions and temporary construction zones. Several companies aim to release autonomous vehicle technologies between 2014 and 2020.
A presentation given at the 2016 Traffic Safety Conference during Closing Session: Technologies Enhancing Transportation Safety. By Roger Berg, Vice President, North America Research and Development, Denso International America, Inc.
This document presents a method for using the video game Grand Theft Auto 5 (GTA 5) to generate datasets for training neural networks and other machine learning models for autonomous vehicle control. GTA 5 features highly realistic graphics and an extensive road network with various environments, vehicles, pedestrians and weather conditions. The author describes extracting bounding boxes around objects, pixel maps for semantic segmentation, and lane position indicators from screenshots within GTA 5 to compile datasets for training computer vision and world modeling systems essential for autonomous driving. Functions are proposed for automatically collecting these various data types to efficiently generate large, diverse datasets for advancing machine learning in autonomous vehicles.
Photogrammetry for Architecture and ConstructionDat Lien
The document discusses photogrammetry workflows for capturing exterior and interior architectural spaces. It covers topics such as photogrammetry basics, data capture including vehicle payloads and mission planning, data processing software options, and output formats including point clouds, 3D meshes, and methods for integrating the data into BIM platforms. Sample projects demonstrate the process and potential applications like clash detection and virtual tours are presented.
Google driverless car technical seminar report (.docx)gautham p
Google Driverless Car is the latest technology or innovation that is going to hit the market in the coming years.
This report is especially for mechanical engineering students.
The document discusses Google's driverless car project. It provides an introduction to autonomous vehicles and describes some of the key technologies used in Google's cars, including laser range finders, cameras, radars, ultrasonic sensors, and GPS. The technologies work together to map the vehicle's environment, plan a safe route, and navigate while avoiding obstacles. Some advantages are fewer accidents and a smoother ride, though limitations include an inability to detect all hazards and potential security issues from hackers. In conclusion, autonomous vehicles may increase safety and improve traffic conditions by removing human error.
This document describes a lane detection and obstacle avoidance system developed using Matlab. A single 180 degree fish eye camera and LIDAR sensor are used. Lane detection is implemented using Hough line and Hough transform to detect lane markers. Obstacle avoidance is done using a SICK LIDAR sensor to detect objects within a buffer zone. The system displays offset distance values from the center of the lane to determine if the vehicle stays within its lane.
CarSafe is a dual-camera smartphone app that aims to alert drowsy and distracted drivers. It uses the front and rear cameras along with sensors to detect dangerous driving events like drowsy driving, tailgating, and lane weaving. The paper describes CarSafe's architecture which includes pipelines for driver, road, and car classification. It also discusses the challenges of real-time dual camera processing on mobile and how CarSafe addresses these through techniques like context-driven camera switching and multicore computation planning. An evaluation shows CarSafe can accurately detect dangerous events with overall precision and recall of 83% and 75%.
This document discusses lane detection and obstacle avoidance techniques for autonomous vehicles. It describes using a fish eye camera and LIDAR sensor for lane detection and obstacle avoidance. For lane detection, a modified lane marking technique detects lane edges and offsets. Hough transforms are used to detect lane markers from camera images. Obstacles are detected using LIDAR distance measurements. The document outlines the lane detection process of filtering, edge detection, and line detection using Hough transforms to identify lane boundaries and position the vehicle within its lane.
The document discusses autonomous or self-driving cars. It describes how autonomous cars use sensors like LIDAR, radar, cameras and ultrasonic sensors along with GPS and an inertial measurement unit to navigate without human intervention. The central computer combines data from these sensors to construct a 3D map of the vehicle's surroundings and control systems like steering and braking. Major companies developing autonomous vehicle technology include Google, Audi, BMW, Ford and General Motors.
From the invention of the car there is a great relation between human and car. Because by the invention of the car the automobile industry was established, by this car the traveling time from one place to another place is reduced. The car brings royalty from the invention. As cars are coming on roads at that time there are so many accidents are occurring due to lack of driving knowledge & drink driving and soon, In that view only the Google took a great project, i.e. Google Driverless Car in these the Google puts the technology in the car, that technology was Artificial Intelligence with Google map view. The input video camera was fixed beside the front mirror inside the car, A LIDAR sensor was fixed on the top of the vehicle, RADAR sensor on the front of the vehicle and a position sensor attached to one of the rear wheels that helps locate the cars position on the map, The Computer, Router, Switch, Fan, Inverter, rear Monitor, Topcon, Velodyne, Applanix and Battery are kept inside the car.
These all components are connected to computer’s CPU and the monitor is fixed on beside of the driver seat, these we can observe in that monitor and can operate all the operations.
SLAM (simultaneous localization and mapping) uses sensor data from the front-end like LIDARs, cameras, IMUs, and GPS along with mathematical models in the back-end to estimate a robot's position and build a map in real-time. Common SLAM sensors supported in ROS include LIDARs, RADAR, cameras, encoders, IMUs, and GPS for estimating position and distances, while the back-end performs optimization, localization, and map building. Sensors provide input data for the front-end and back-end SLAM processes to estimate position and construct a map.
The document discusses LiDAR processing for road network asset inventory. It outlines an algorithm developed for extracting road edges from LiDAR point clouds without manual input. It also discusses using the extracted road edges to develop a road surface extraction algorithm. Pole detection and extraction methods are also examined. The goal is to develop automated feature extraction from mobile mapping LiDAR and image data for road inventory purposes.
Summer research project that include evaluate two online camera calibration algorithms and use the algorithm with better test result to perform back-projection and geo-location for pedestrian to be virtualized in 3D model.
This document discusses Google's driverless car technology. It describes the key components of Google's driverless cars, including Lidar, radar, cameras, and ultrasonic sensors that allow the cars to navigate roads autonomously. The document also outlines some of the major challenges facing driverless car technology, such as difficulties operating in heavy rain or differentiating between objects. The goal of Google's driverless car is to reduce accidents caused by human error by developing fully autonomous vehicles.
The document discusses Qualcomm's automotive technologies including biometrics, 3D vision capabilities, ADAS, autonomous driving solutions, and human-centric vision products. It describes Qualcomm's Snapdragon platforms that provide capabilities like telematics, infotainment, driver monitoring systems, and autonomous driving stacks to power advanced driver assistance systems and autonomous vehicles. The platforms utilize Qualcomm's CPU, GPU, DSP, and other technologies to deliver solutions spanning biometrics, computer vision, ADAS, autonomous driving, and more.
The document discusses driverless vehicle technologies, including how they detect traffic lights and sense their surroundings. It describes sensors like radar, lidar, cameras and GPS that provide input to control systems. The control systems analyze sensor data to identify paths and obstacles. Technologies like automatic braking, electronic stability control and cruise control help control the vehicle. The processor makes sense of sensor data to guide actuators that control the vehicle without driver assistance.
DOME: Recommendations for supervised machine learning validation in biologyDmytro Fishman
Invited talk for the special session "Towards standardizing machine learning in life sciences: the FAIR principles and the DOME recommendations"
DOME Nature paper: https://www.nature.com/articles/s41592-021-01205-4
The document provides tips for effective presentations and uses examples from Gregor Mendel's work to illustrate those tips. It suggests focusing on one concept at a time, making slides that guide the story, and getting to know the audience. The document also summarizes a presentation tool called PAWER that allows uploading gene expression data files, normalizing samples, identifying differential proteins, and linking to other analysis tools.
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The document provides an introduction to self-driving cars from Dr. Punnu Phairatt, a self-driving car engineer. It begins with an overview of the different levels of autonomy in self-driving cars from fully manual to fully autonomous without human intervention. It then discusses the key components that enable self-driving functionality, including sensors, localization, perception, decision making, navigation, and control. The rest of the document includes details on challenges in autonomous driving, different software approaches, and a proposed system design architecture with examples of localization, decision making, and navigation modules. It concludes with a quick demo of self-driving software and a discussion on further developing open source self-driving car software.
This document discusses automated or driverless cars. It describes how driverless cars use sensors like LIDAR and radar along with artificial intelligence, GPS, and Google Maps to navigate without human intervention. The car's AI software is connected to all sensors and controls systems like steering and brakes based on input from sensors and maps. Major companies developing driverless car technology include Google, GM, Ford, Audi, BMW, Volkswagen and Volvo. Benefits include eliminating accidents from human error, improving traffic flow, and allowing passengers to work or rest while the car drives itself.
The document provides an overview of driverless or autonomous vehicles. It discusses the history and components of these vehicles, including sensors like LIDAR, radar and cameras. The document explains how artificial intelligence analyzes sensor data to navigate autonomously. Potential advantages are reduced accidents and increased road capacity, while obstacles include handling various weather conditions and temporary construction zones. Several companies aim to release autonomous vehicle technologies between 2014 and 2020.
A presentation given at the 2016 Traffic Safety Conference during Closing Session: Technologies Enhancing Transportation Safety. By Roger Berg, Vice President, North America Research and Development, Denso International America, Inc.
This document presents a method for using the video game Grand Theft Auto 5 (GTA 5) to generate datasets for training neural networks and other machine learning models for autonomous vehicle control. GTA 5 features highly realistic graphics and an extensive road network with various environments, vehicles, pedestrians and weather conditions. The author describes extracting bounding boxes around objects, pixel maps for semantic segmentation, and lane position indicators from screenshots within GTA 5 to compile datasets for training computer vision and world modeling systems essential for autonomous driving. Functions are proposed for automatically collecting these various data types to efficiently generate large, diverse datasets for advancing machine learning in autonomous vehicles.
Photogrammetry for Architecture and ConstructionDat Lien
The document discusses photogrammetry workflows for capturing exterior and interior architectural spaces. It covers topics such as photogrammetry basics, data capture including vehicle payloads and mission planning, data processing software options, and output formats including point clouds, 3D meshes, and methods for integrating the data into BIM platforms. Sample projects demonstrate the process and potential applications like clash detection and virtual tours are presented.
Google driverless car technical seminar report (.docx)gautham p
Google Driverless Car is the latest technology or innovation that is going to hit the market in the coming years.
This report is especially for mechanical engineering students.
The document discusses Google's driverless car project. It provides an introduction to autonomous vehicles and describes some of the key technologies used in Google's cars, including laser range finders, cameras, radars, ultrasonic sensors, and GPS. The technologies work together to map the vehicle's environment, plan a safe route, and navigate while avoiding obstacles. Some advantages are fewer accidents and a smoother ride, though limitations include an inability to detect all hazards and potential security issues from hackers. In conclusion, autonomous vehicles may increase safety and improve traffic conditions by removing human error.
This document describes a lane detection and obstacle avoidance system developed using Matlab. A single 180 degree fish eye camera and LIDAR sensor are used. Lane detection is implemented using Hough line and Hough transform to detect lane markers. Obstacle avoidance is done using a SICK LIDAR sensor to detect objects within a buffer zone. The system displays offset distance values from the center of the lane to determine if the vehicle stays within its lane.
CarSafe is a dual-camera smartphone app that aims to alert drowsy and distracted drivers. It uses the front and rear cameras along with sensors to detect dangerous driving events like drowsy driving, tailgating, and lane weaving. The paper describes CarSafe's architecture which includes pipelines for driver, road, and car classification. It also discusses the challenges of real-time dual camera processing on mobile and how CarSafe addresses these through techniques like context-driven camera switching and multicore computation planning. An evaluation shows CarSafe can accurately detect dangerous events with overall precision and recall of 83% and 75%.
This document discusses lane detection and obstacle avoidance techniques for autonomous vehicles. It describes using a fish eye camera and LIDAR sensor for lane detection and obstacle avoidance. For lane detection, a modified lane marking technique detects lane edges and offsets. Hough transforms are used to detect lane markers from camera images. Obstacles are detected using LIDAR distance measurements. The document outlines the lane detection process of filtering, edge detection, and line detection using Hough transforms to identify lane boundaries and position the vehicle within its lane.
The document discusses autonomous or self-driving cars. It describes how autonomous cars use sensors like LIDAR, radar, cameras and ultrasonic sensors along with GPS and an inertial measurement unit to navigate without human intervention. The central computer combines data from these sensors to construct a 3D map of the vehicle's surroundings and control systems like steering and braking. Major companies developing autonomous vehicle technology include Google, Audi, BMW, Ford and General Motors.
From the invention of the car there is a great relation between human and car. Because by the invention of the car the automobile industry was established, by this car the traveling time from one place to another place is reduced. The car brings royalty from the invention. As cars are coming on roads at that time there are so many accidents are occurring due to lack of driving knowledge & drink driving and soon, In that view only the Google took a great project, i.e. Google Driverless Car in these the Google puts the technology in the car, that technology was Artificial Intelligence with Google map view. The input video camera was fixed beside the front mirror inside the car, A LIDAR sensor was fixed on the top of the vehicle, RADAR sensor on the front of the vehicle and a position sensor attached to one of the rear wheels that helps locate the cars position on the map, The Computer, Router, Switch, Fan, Inverter, rear Monitor, Topcon, Velodyne, Applanix and Battery are kept inside the car.
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SLAM (simultaneous localization and mapping) uses sensor data from the front-end like LIDARs, cameras, IMUs, and GPS along with mathematical models in the back-end to estimate a robot's position and build a map in real-time. Common SLAM sensors supported in ROS include LIDARs, RADAR, cameras, encoders, IMUs, and GPS for estimating position and distances, while the back-end performs optimization, localization, and map building. Sensors provide input data for the front-end and back-end SLAM processes to estimate position and construct a map.
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This document discusses Google's driverless car technology. It describes the key components of Google's driverless cars, including Lidar, radar, cameras, and ultrasonic sensors that allow the cars to navigate roads autonomously. The document also outlines some of the major challenges facing driverless car technology, such as difficulties operating in heavy rain or differentiating between objects. The goal of Google's driverless car is to reduce accidents caused by human error by developing fully autonomous vehicles.
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42. Inertial Measurement Unit (IMU)
Measures acceleration in all axes and rotational components
Roll
Yaw
43. Inertial Measurement Unit (IMU)
Measures acceleration in all axes and rotational components
Roll
Yaw
Pitch
44. Inertial Measurement Unit (IMU)
Measures acceleration in all axes and rotational components
Roll
Yaw
Pitch
6-DOF (degrees of freedom)
Forward
Back
Left
Right
Down
Up