Google Self Driving Cars
The Google Self-Driving Car is a project by Google that involves developing technology for autonomous cars. The software powering Google's cars is called Google Chauffeur. Lettering on the side of each car identifies it as a "self-driving car". The project is currently being led by Google engineer 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. 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 states and the District of Columbia allowing driverless cars. The U.S. 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. In July 2014, the city of Coeur d'Alene, Idaho adopted a robotics ordinance that includes provisions to allow for self-driving cars.
Videos
https://www.youtube.com/channel/UCCLyNDhxwpqNe3UeEmGHl8g
Google Self Driving Cars
The Google Self-Driving Car is a project by Google that involves developing technology for autonomous cars. The software powering Google's cars is called Google Chauffeur. Lettering on the side of each car identifies it as a "self-driving car". The project is currently being led by Google engineer 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. 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 states and the District of Columbia allowing driverless cars. The U.S. 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. In July 2014, the city of Coeur d'Alene, Idaho adopted a robotics ordinance that includes provisions to allow for self-driving cars.
Videos
https://www.youtube.com/channel/UCCLyNDhxwpqNe3UeEmGHl8g
Self driving cars are the future and we must be ready for it whether we like it or not.
This ppt covers self driving cars and the latest technology used in them
Research presentation on Autonomous Driving. Direction perception approach.
Research work by Princeton University group.
Note: Link given in the presentation
Ground breaking innovations like Advanced Driver Assistance System (ADAS) makes driving easier and safer on congested roads. The whitepaper details how FPGA technology emerges as a complete solution for ADAS.
An autonomous vehicle is a kind of vehicle which can drive itself to the destination without any human
conduction. This is also known as driverless vehicle, self-driving vehicle or robot vehicle. Autonomous
vehicles require the combination of various sensors to detect their surroundings and interpret the
information to identify the appropriate navigation path and the obstacles in the way.
Modern vehicles provide some autonomous features like speed controls, emergency braking or keeping
the vehicle into the lane. Here, differences remain between a fully autonomous vehicle on one hand
and driver assistance technologies on the other hand.
After decades of anticipation, practical self-driving cars are here. Drive.ai will deploy a self-driving car service for public use in Texas starting in July.
We can continue pushing self-driving forward by focusing on three key elements: industry-leading AI technology, local partnerships, and people-centric safety.
it is a presentation on auto driving car or driverless car . it is a group presentation on auto driving car for power system analysis course from American International University Bangladesh (AIUB) .
Self driving cars are the future and we must be ready for it whether we like it or not.
This ppt covers self driving cars and the latest technology used in them
Research presentation on Autonomous Driving. Direction perception approach.
Research work by Princeton University group.
Note: Link given in the presentation
Ground breaking innovations like Advanced Driver Assistance System (ADAS) makes driving easier and safer on congested roads. The whitepaper details how FPGA technology emerges as a complete solution for ADAS.
An autonomous vehicle is a kind of vehicle which can drive itself to the destination without any human
conduction. This is also known as driverless vehicle, self-driving vehicle or robot vehicle. Autonomous
vehicles require the combination of various sensors to detect their surroundings and interpret the
information to identify the appropriate navigation path and the obstacles in the way.
Modern vehicles provide some autonomous features like speed controls, emergency braking or keeping
the vehicle into the lane. Here, differences remain between a fully autonomous vehicle on one hand
and driver assistance technologies on the other hand.
After decades of anticipation, practical self-driving cars are here. Drive.ai will deploy a self-driving car service for public use in Texas starting in July.
We can continue pushing self-driving forward by focusing on three key elements: industry-leading AI technology, local partnerships, and people-centric safety.
it is a presentation on auto driving car or driverless car . it is a group presentation on auto driving car for power system analysis course from American International University Bangladesh (AIUB) .
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.
The Google driverless car is a project by Google that involves developing technology for autonomous cars.
The Google Driverless Car is like any car, but:
It can steer itself while looking out for obstacles.
It can accelerate itself to the correct speed limit.
It can stop and go itself based on any traffic
The software powering Google's cars is called Google Chauffeur.
It can take its passengers anywhere it wants to go safely, legally, and comfortably.
Currently being led by Sebastian Thrun ,former director of Stanford Artificial Intelligence Laboratory and co-inventor of Google’s Street View.
autonomous car, self driving car, presentation on google driving car, sensor used in car , future car, four wheeler car , colloquium, mechanical engineering, engineering technology ,b.tech, automotive engineering, california based car, self control car
Epistemic Interaction - tuning interfaces to provide information for AI supportAlan Dix
Paper presented at SYNERGY workshop at AVI 2024, Genoa, Italy. 3rd June 2024
https://alandix.com/academic/papers/synergy2024-epistemic/
As machine learning integrates deeper into human-computer interactions, the concept of epistemic interaction emerges, aiming to refine these interactions to enhance system adaptability. This approach encourages minor, intentional adjustments in user behaviour to enrich the data available for system learning. This paper introduces epistemic interaction within the context of human-system communication, illustrating how deliberate interaction design can improve system understanding and adaptation. Through concrete examples, we demonstrate the potential of epistemic interaction to significantly advance human-computer interaction by leveraging intuitive human communication strategies to inform system design and functionality, offering a novel pathway for enriching user-system engagements.
"Impact of front-end architecture on development cost", Viktor TurskyiFwdays
I have heard many times that architecture is not important for the front-end. Also, many times I have seen how developers implement features on the front-end just following the standard rules for a framework and think that this is enough to successfully launch the project, and then the project fails. How to prevent this and what approach to choose? I have launched dozens of complex projects and during the talk we will analyze which approaches have worked for me and which have not.
State of ICS and IoT Cyber Threat Landscape Report 2024 previewPrayukth K V
The IoT and OT threat landscape report has been prepared by the Threat Research Team at Sectrio using data from Sectrio, cyber threat intelligence farming facilities spread across over 85 cities around the world. In addition, Sectrio also runs AI-based advanced threat and payload engagement facilities that serve as sinks to attract and engage sophisticated threat actors, and newer malware including new variants and latent threats that are at an earlier stage of development.
The latest edition of the OT/ICS and IoT security Threat Landscape Report 2024 also covers:
State of global ICS asset and network exposure
Sectoral targets and attacks as well as the cost of ransom
Global APT activity, AI usage, actor and tactic profiles, and implications
Rise in volumes of AI-powered cyberattacks
Major cyber events in 2024
Malware and malicious payload trends
Cyberattack types and targets
Vulnerability exploit attempts on CVEs
Attacks on counties – USA
Expansion of bot farms – how, where, and why
In-depth analysis of the cyber threat landscape across North America, South America, Europe, APAC, and the Middle East
Why are attacks on smart factories rising?
Cyber risk predictions
Axis of attacks – Europe
Systemic attacks in the Middle East
Download the full report from here:
https://sectrio.com/resources/ot-threat-landscape-reports/sectrio-releases-ot-ics-and-iot-security-threat-landscape-report-2024/
Essentials of Automations: Optimizing FME Workflows with ParametersSafe Software
Are you looking to streamline your workflows and boost your projects’ efficiency? Do you find yourself searching for ways to add flexibility and control over your FME workflows? If so, you’re in the right place.
Join us for an insightful dive into the world of FME parameters, a critical element in optimizing workflow efficiency. This webinar marks the beginning of our three-part “Essentials of Automation” series. This first webinar is designed to equip you with the knowledge and skills to utilize parameters effectively: enhancing the flexibility, maintainability, and user control of your FME projects.
Here’s what you’ll gain:
- Essentials of FME Parameters: Understand the pivotal role of parameters, including Reader/Writer, Transformer, User, and FME Flow categories. Discover how they are the key to unlocking automation and optimization within your workflows.
- Practical Applications in FME Form: Delve into key user parameter types including choice, connections, and file URLs. Allow users to control how a workflow runs, making your workflows more reusable. Learn to import values and deliver the best user experience for your workflows while enhancing accuracy.
- Optimization Strategies in FME Flow: Explore the creation and strategic deployment of parameters in FME Flow, including the use of deployment and geometry parameters, to maximize workflow efficiency.
- Pro Tips for Success: Gain insights on parameterizing connections and leveraging new features like Conditional Visibility for clarity and simplicity.
We’ll wrap up with a glimpse into future webinars, followed by a Q&A session to address your specific questions surrounding this topic.
Don’t miss this opportunity to elevate your FME expertise and drive your projects to new heights of efficiency.
UiPath Test Automation using UiPath Test Suite series, part 3DianaGray10
Welcome to UiPath Test Automation using UiPath Test Suite series part 3. In this session, we will cover desktop automation along with UI automation.
Topics covered:
UI automation Introduction,
UI automation Sample
Desktop automation flow
Pradeep Chinnala, Senior Consultant Automation Developer @WonderBotz and UiPath MVP
Deepak Rai, Automation Practice Lead, Boundaryless Group and UiPath MVP
Slack (or Teams) Automation for Bonterra Impact Management (fka Social Soluti...Jeffrey Haguewood
Sidekick Solutions uses Bonterra Impact Management (fka Social Solutions Apricot) and automation solutions to integrate data for business workflows.
We believe integration and automation are essential to user experience and the promise of efficient work through technology. Automation is the critical ingredient to realizing that full vision. We develop integration products and services for Bonterra Case Management software to support the deployment of automations for a variety of use cases.
This video focuses on the notifications, alerts, and approval requests using Slack for Bonterra Impact Management. The solutions covered in this webinar can also be deployed for Microsoft Teams.
Interested in deploying notification automations for Bonterra Impact Management? Contact us at sales@sidekicksolutionsllc.com to discuss next steps.
UiPath Test Automation using UiPath Test Suite series, part 4DianaGray10
Welcome to UiPath Test Automation using UiPath Test Suite series part 4. In this session, we will cover Test Manager overview along with SAP heatmap.
The UiPath Test Manager overview with SAP heatmap webinar offers a concise yet comprehensive exploration of the role of a Test Manager within SAP environments, coupled with the utilization of heatmaps for effective testing strategies.
Participants will gain insights into the responsibilities, challenges, and best practices associated with test management in SAP projects. Additionally, the webinar delves into the significance of heatmaps as a visual aid for identifying testing priorities, areas of risk, and resource allocation within SAP landscapes. Through this session, attendees can expect to enhance their understanding of test management principles while learning practical approaches to optimize testing processes in SAP environments using heatmap visualization techniques
What will you get from this session?
1. Insights into SAP testing best practices
2. Heatmap utilization for testing
3. Optimization of testing processes
4. Demo
Topics covered:
Execution from the test manager
Orchestrator execution result
Defect reporting
SAP heatmap example with demo
Speaker:
Deepak Rai, Automation Practice Lead, Boundaryless Group and UiPath MVP
GDG Cloud Southlake #33: Boule & Rebala: Effective AppSec in SDLC using Deplo...James Anderson
Effective Application Security in Software Delivery lifecycle using Deployment Firewall and DBOM
The modern software delivery process (or the CI/CD process) includes many tools, distributed teams, open-source code, and cloud platforms. Constant focus on speed to release software to market, along with the traditional slow and manual security checks has caused gaps in continuous security as an important piece in the software supply chain. Today organizations feel more susceptible to external and internal cyber threats due to the vast attack surface in their applications supply chain and the lack of end-to-end governance and risk management.
The software team must secure its software delivery process to avoid vulnerability and security breaches. This needs to be achieved with existing tool chains and without extensive rework of the delivery processes. This talk will present strategies and techniques for providing visibility into the true risk of the existing vulnerabilities, preventing the introduction of security issues in the software, resolving vulnerabilities in production environments quickly, and capturing the deployment bill of materials (DBOM).
Speakers:
Bob Boule
Robert Boule is a technology enthusiast with PASSION for technology and making things work along with a knack for helping others understand how things work. He comes with around 20 years of solution engineering experience in application security, software continuous delivery, and SaaS platforms. He is known for his dynamic presentations in CI/CD and application security integrated in software delivery lifecycle.
Gopinath Rebala
Gopinath Rebala is the CTO of OpsMx, where he has overall responsibility for the machine learning and data processing architectures for Secure Software Delivery. Gopi also has a strong connection with our customers, leading design and architecture for strategic implementations. Gopi is a frequent speaker and well-known leader in continuous delivery and integrating security into software delivery.
DevOps and Testing slides at DASA ConnectKari Kakkonen
My and Rik Marselis slides at 30.5.2024 DASA Connect conference. We discuss about what is testing, then what is agile testing and finally what is Testing in DevOps. Finally we had lovely workshop with the participants trying to find out different ways to think about quality and testing in different parts of the DevOps infinity loop.
2. Index
Introduction
History
Reason behind the making
Working of Driverless Cars
Components
Current Status
Self-Driving Car Pros and Cons
Conclusion
3. Introduction
A self-driving car (sometimes called an autonomous car or driverless car) is a
vehicle that uses a combination of sensors, cameras, radar and Artificial Intelligence
(AI) to travel between destinations without a human operator.
4. Introduction
The Google self-driving vehicle. Google's self-driving cars all have test
drivers at the wheel. They have been developed by Google X as a part of
its project to develop technology for electric cars.
Autonomous vehicles and driver-assisting technologies have the potential
to reduce crashes, prevent injuries, and save lives.
Google wants to design a new prototype of its driverless car. This would
be a car that has no steering wheel, no gas or brake pedals and thus is
100% autonomous.
5. History
Houdina Radio Control Co. publicly demonstrated the radio-controlled driver-
less car ‘American Wonder’ in New York City streets.
The radio-operated automobile, American Wonder, 1925.
6. History
In 1939 GM’s exhibit, the concept of autonomous vehicles which was an electric vehicle
guided by radio-controlled electromagnetic fields in New York World’s Fair.
8. Reason behind making
A car operated by computers could somehow be safer, car accidents have been
caused by some sort of human error, be it speeding, driving recklessly,
inattentiveness.
A self-driving car would allow them to get some work done, knock a few emails out,
or even get a little extra sleep if they have to wake up early to get to work.
Because self-driving cars are safer, they’ll cut down on any accident-induced costs,
which means insurance premiums.
Self-driving cars could be of huge benefit to the environment. transportation, in
general, was responsible for over half of the carbon monoxide and nitrogen oxide air
pollution as well as a quarter of the hydrocarbons emitted into our atmosphere. While
many self-driving cars might still emit these same materials, their improved efficiency
would be a huge step forward toward a cleaner future.
9. Working of Driverless Cars
It is controlled by computer.
It requires input or instruction from human
After getting input or instruction it will drive automatically
10. Components
GPS (Global Positioning System)
The GPS in driverless cars is not much more different than Google Maps' navigational
software.
The major difference is that driverless cars require GPS to navigate the car, while human
driven cars do not.
GPS software is important because it defines
the "mission" of the autonomous vehicle by
setting a start and end point of the drive.
The GPS works in conjunction with radars,
sensors, LIDAR and other HD mapping
software.
11. Components
Video Cameras
Video camera is installed at the top of the front glass near the rear mirror view.
Video cameras which looks for hazards such as
pedestrians, cyclists, other motorist, and reads
the road signs and detect the traffic lights.
The technology behind these cameras
function similar to the human eye
12. Components
LIDAR (Laser Illuminating Detection and Ranging)
The LIDAR unit, provides driverless cars with highly accurate long range detection
which ranges of up to 100 meters.
As it is spins, it continuously scans the world around the
car and builds a 3D omni-directional.
The rotating LIDAR units are normally mounted to the top of the car, providing 360-
degree view.
This unit generates raw information about the world, which is then sent to the car's
brain to process.
13. Components
Radar
Autonomous cars have radar sensor units (two in the front and two in the back).
These help the vehicle detect road dynamics such as traffic delays, vehicle collisions, and
other obstacles, by sending a signal processor to apply the brakes and/or move out of the
way.
This technology works in conjunction with other features on the car such as
inertial measurement units,
gyroscopes, and
wheel encoder
to send accurate signals
to the processing unit (i.e. brain) of
the vehicle.
15. Self-Driving Car Pros and Cons
Pros:
1. Mobility for those unable to
drive.
2. Reduced human induced
vehicle collisions.
3. Time savings.
4. Smaller roads and lesser
congestion.
5. Reduced parking strain.
Cons:
1. Initial purchase price.
2. Sensitive data mining and
hacking.
3. Unemployment implications.
4. Accountability and adaptability.
5. Reliance on technology
16. Conclusion
Driverless cars appear to be an important next step in transportation technology.
Developments in autonomous cars is continuing and the software in the car is
continuing to be updated.
People who currently reject self-driving cars would’ve said no to modern technology
and automatic systems.
If the people’s thought hasn’t changed about the self-driving cars being safe, these cars
are already safe and are becoming safer. Only if they believe and give a try to
technology, they get to enjoy the luxury of computerized driving.