NeurIPS 2020 ML4AD - Paper 13 - CIL with Sensor FusionHesham Eraqi
Conditional Imitation Learning Driving Considering Camera and LiDAR Fusion
Hesham M. Eraqi, Mohamed N. Moustafa, and Jens Honer
heraqi@acuegypt.edu, m.moustafa@aucegypt.edu, jens.honer@valeo.com
In this class we introduce the various environmentally sustainable alternatives to power generation. Solar and wind technologies are discussed for power generation for residential and commercial purposes. Ethanol and fuel cell technologies are discussed for the purpose of ensuring clean transportation systems. The latter provided the possibility of cars running on water.
Deep reinforcement learning framework for autonomous drivingGopikaGopinath5
Motivated by the successful demonstrations of learning of Atari games and Go by Google DeepMind, it is possible to propose a framework for autonomous driving using deep reinforcement learning.
It incorporates Recurrent Neural Networks for information integration, enabling the car to handle partially observable scenarios.
Driverless Vehicles: Future Outlook on Intelligent TransportationIJERA Editor
Numerous technologies have been deployed to assist and manage transportation .But recent concerted efforts in academia andindustry point to a paradigm shift in intelligent transportation systems. Vehicles will carry computing and communication platforms,and will have enhanced sensing capabilities .They will enable new versatile systemsthat enhance transportation efficiency. This article surveys the sate-of-art approaches towards the future outlook on intelligent transportation. Current capabilities as well as limitations and opportunities of key enabling technologies are reviewed along with details of numerous notable projects that have been done around the world. Finally report also reviews the legal and regulatory uncertainties.
NeurIPS 2020 ML4AD - Paper 13 - CIL with Sensor FusionHesham Eraqi
Conditional Imitation Learning Driving Considering Camera and LiDAR Fusion
Hesham M. Eraqi, Mohamed N. Moustafa, and Jens Honer
heraqi@acuegypt.edu, m.moustafa@aucegypt.edu, jens.honer@valeo.com
In this class we introduce the various environmentally sustainable alternatives to power generation. Solar and wind technologies are discussed for power generation for residential and commercial purposes. Ethanol and fuel cell technologies are discussed for the purpose of ensuring clean transportation systems. The latter provided the possibility of cars running on water.
Deep reinforcement learning framework for autonomous drivingGopikaGopinath5
Motivated by the successful demonstrations of learning of Atari games and Go by Google DeepMind, it is possible to propose a framework for autonomous driving using deep reinforcement learning.
It incorporates Recurrent Neural Networks for information integration, enabling the car to handle partially observable scenarios.
Driverless Vehicles: Future Outlook on Intelligent TransportationIJERA Editor
Numerous technologies have been deployed to assist and manage transportation .But recent concerted efforts in academia andindustry point to a paradigm shift in intelligent transportation systems. Vehicles will carry computing and communication platforms,and will have enhanced sensing capabilities .They will enable new versatile systemsthat enhance transportation efficiency. This article surveys the sate-of-art approaches towards the future outlook on intelligent transportation. Current capabilities as well as limitations and opportunities of key enabling technologies are reviewed along with details of numerous notable projects that have been done around the world. Finally report also reviews the legal and regulatory uncertainties.
Cyber-physical systems Industrial applications in the CPSwarm ProjectAlessandra Bagnato
CPS and Cyber-Physical Systems of Systems (CPSoS) are increasingly playing the role of foundational building blocks for bringing adaptive intelligence to processes and environments, in several application domains ranging from Smart Mobility, to Smart Health, Smart Cities and Smart Production. Due to the
increasing pervasiveness of CPS, issues related to effective design of solutions, able to reach predefined goals flexibly, reliably and adapting to changing surrounding conditions, become challenging and worth of further investigation. While increasing the CPS adoption results in increasingly mature solutions for their development, a single, consistent, science of system integration for CPS has not yet been consolidated.
As a matter of fact, the increasing interactions amongst different
CPS are starting to generate unpredicted behaviours and emerging properties, often leading to unforeseen and/or undesired results. These interactions could become an advantage if they were explicitly managed, and accounted, since the early design stages. The CPSwarm project,
presented in this lecture, aims at tackling these kinds of challenges by easing development and integration of complex herds of heterogeneous CPS. Thanks to CPSwarm, systems designed through a combination of existing and emerging tools, will collaborate on the
basis of local policies and exhibit a collective behaviour capable of solving complex, real-world, problems. Three real-world use cases will demonstrate the validity of foundational assumptions of the presented approach as well as the viability of the developed tools and methodologies.
CPSwarm will demonstrate the viability of the proposed approach on 3 complimentary, yet di_erent, use cases targeted at: (a) swarms of (mixed) robotic vehicles (e.g. Unmanned Aerial Vehicles (UAV) and rovers), (b) automotive CPS systems for freight vehicles and (c) swarm logistics.
All scenarios are characterized by the presence of heterogeneous CPS interacting together and showing emerging behaviors difficult to predict with traditional approaches and will be presented in the lecture.
Autonomous driving system using proximal policy optimization in deep reinforc...IAESIJAI
Autonomous driving is one solution that can minimize and even prevent
accidents. In autonomous driving, the vehicle must know the surrounding
environment and move under the provisions and situations. We build an
autonomous driving system using proximal policy optimization (PPO) in
deep reinforcement learning, with PPO acting as an instinct for the agent to
choose an action. The instinct will be updated continuously until the agent
reaches the destination from the initial point. We use five sensory inputs for
the agent to accelerate, turn the steer, hit the brakes, avoid the walls, detect
the initial point, and reach the destination point. We evaluated our proposed
autonomous driving system in a simulation environment with several
branching tracks, reflecting a real-world setting. For our driving simulation
purpose in this research, we use the Unity3D engine to construct the dataset
(in the form of a road track) and the agent model (in the form of a car). Our
experimental results firmly indicate our agent can successfully control a
vehicle to navigate to the destination point.
In this paper, a project is described which is a 2-D
modelled version of a car that will learn how to drive itself. It
will have to figure everything out on its own. In addition, to
achieve that the simulator contains a car running
simultaneously &can be controlled by different control
algorithms - heuristic, reinforcement learning-based, etc. For
each dynamic input, the Reinforcement- Learning modifies
new patterns. Ultimately, Reinforcement Learning helps in
maximizing the reward from every state. In this first Part, we
will implement a Reinforcement-Learning model to build an
AI for Self Driving Car. Project will be focusing on the brain
of the car not any graphics. The car will detect obstacles and
take basic actions. To make autonomous car or self-driving
car a reality, some of the factors to be considered are human
safety and quality of life.
Problems in Autonomous Driving System of Smart Cities in IoTijtsrd
This paper focuses on the problems and challenges during self driving. In the modern era, technologies are getting advanced day by day. The field of smart city has introduced a new technology called ""Autonomous Driving"". Autonomous driving can be defined as Self Driving, Automated Vehicle. Google has started working on this type of system since 2010 and still in the phase of making changes in this technology to take it to a higher level. Any technology can reach up to an advanced level but it cannot provide a full fledged result. This paper facilitates the researchers to understand the problems, challenges and issues related to this technology. Shweta S. Darekar | Dr. Anandhi Giri ""Problems in Autonomous Driving System of Smart Cities in IoT"" Published in International Journal of Trend in Scientific Research and Development (ijtsrd), ISSN: 2456-6470, Volume-4 | Issue-2 , February 2020,
URL: https://www.ijtsrd.com/papers/ijtsrd30079.pdf
Paper Url : https://www.ijtsrd.com/computer-science/other/30079/problems-in-autonomous-driving-system-of-smart-cities-in-iot/shweta-s-darekar
The development of driverless vehicles is fast, and the technology has the potential to significantly affect the transport system, society and environment
The Autonomous car is about to enter the mass-market. The question is not about when it will happen but in which conditions, under which form or who will be the first car manufacturer to release an efficient and reliable final product. Entirely unexpected ways to deal with building up the AI frameworks for self-driving vehicles exist and most of them are horribly best in class and with extremely high equipment needs. The appropriate response presented during this paper proposes the AI fundamentally based framework to be as simple as conceivable with low equipment needs. A straight forward three layers profound, totally associated neural system was prepared to outline pictures from a forward looking QVGA camera to directing orders. Upheld an information picture the neural system should settle on one among the four offered orders (Forward, Left, Right or Stop). With least of the instructing information (250 pictures) the framework figured out how to follow the street ahead and keep in its path.The framework precisely learns essential street alternatives with exclusively the directing point in light of the fact that the contribution from the human driver. it had been near explicitly prepared to watch lines out and about. Contrasted with rather progressively confounded arrangements like express decay of the issue, similar to path identification and the board and convolutional neural systems simply like the conclusion to complete the process of learning arranged by the N-Vidia this technique demonstrated to be amazingly solid and affordable. we will in general attempt to demonstrate that this methodology would bring about better and lower equipment necessities so making the occasion of oneself driving vehicles simpler and more financially savvy. Simple counterfeit neural system, much the same as the one gave during this paper, is sufficient for relatively muddled technique like path keeping.
The Art of the Pitch: WordPress Relationships and SalesLaura Byrne
Clients don’t know what they don’t know. What web solutions are right for them? How does WordPress come into the picture? How do you make sure you understand scope and timeline? What do you do if sometime changes?
All these questions and more will be explored as we talk about matching clients’ needs with what your agency offers without pulling teeth or pulling your hair out. Practical tips, and strategies for successful relationship building that leads to closing the deal.
Cyber-physical systems Industrial applications in the CPSwarm ProjectAlessandra Bagnato
CPS and Cyber-Physical Systems of Systems (CPSoS) are increasingly playing the role of foundational building blocks for bringing adaptive intelligence to processes and environments, in several application domains ranging from Smart Mobility, to Smart Health, Smart Cities and Smart Production. Due to the
increasing pervasiveness of CPS, issues related to effective design of solutions, able to reach predefined goals flexibly, reliably and adapting to changing surrounding conditions, become challenging and worth of further investigation. While increasing the CPS adoption results in increasingly mature solutions for their development, a single, consistent, science of system integration for CPS has not yet been consolidated.
As a matter of fact, the increasing interactions amongst different
CPS are starting to generate unpredicted behaviours and emerging properties, often leading to unforeseen and/or undesired results. These interactions could become an advantage if they were explicitly managed, and accounted, since the early design stages. The CPSwarm project,
presented in this lecture, aims at tackling these kinds of challenges by easing development and integration of complex herds of heterogeneous CPS. Thanks to CPSwarm, systems designed through a combination of existing and emerging tools, will collaborate on the
basis of local policies and exhibit a collective behaviour capable of solving complex, real-world, problems. Three real-world use cases will demonstrate the validity of foundational assumptions of the presented approach as well as the viability of the developed tools and methodologies.
CPSwarm will demonstrate the viability of the proposed approach on 3 complimentary, yet di_erent, use cases targeted at: (a) swarms of (mixed) robotic vehicles (e.g. Unmanned Aerial Vehicles (UAV) and rovers), (b) automotive CPS systems for freight vehicles and (c) swarm logistics.
All scenarios are characterized by the presence of heterogeneous CPS interacting together and showing emerging behaviors difficult to predict with traditional approaches and will be presented in the lecture.
Autonomous driving system using proximal policy optimization in deep reinforc...IAESIJAI
Autonomous driving is one solution that can minimize and even prevent
accidents. In autonomous driving, the vehicle must know the surrounding
environment and move under the provisions and situations. We build an
autonomous driving system using proximal policy optimization (PPO) in
deep reinforcement learning, with PPO acting as an instinct for the agent to
choose an action. The instinct will be updated continuously until the agent
reaches the destination from the initial point. We use five sensory inputs for
the agent to accelerate, turn the steer, hit the brakes, avoid the walls, detect
the initial point, and reach the destination point. We evaluated our proposed
autonomous driving system in a simulation environment with several
branching tracks, reflecting a real-world setting. For our driving simulation
purpose in this research, we use the Unity3D engine to construct the dataset
(in the form of a road track) and the agent model (in the form of a car). Our
experimental results firmly indicate our agent can successfully control a
vehicle to navigate to the destination point.
In this paper, a project is described which is a 2-D
modelled version of a car that will learn how to drive itself. It
will have to figure everything out on its own. In addition, to
achieve that the simulator contains a car running
simultaneously &can be controlled by different control
algorithms - heuristic, reinforcement learning-based, etc. For
each dynamic input, the Reinforcement- Learning modifies
new patterns. Ultimately, Reinforcement Learning helps in
maximizing the reward from every state. In this first Part, we
will implement a Reinforcement-Learning model to build an
AI for Self Driving Car. Project will be focusing on the brain
of the car not any graphics. The car will detect obstacles and
take basic actions. To make autonomous car or self-driving
car a reality, some of the factors to be considered are human
safety and quality of life.
Problems in Autonomous Driving System of Smart Cities in IoTijtsrd
This paper focuses on the problems and challenges during self driving. In the modern era, technologies are getting advanced day by day. The field of smart city has introduced a new technology called ""Autonomous Driving"". Autonomous driving can be defined as Self Driving, Automated Vehicle. Google has started working on this type of system since 2010 and still in the phase of making changes in this technology to take it to a higher level. Any technology can reach up to an advanced level but it cannot provide a full fledged result. This paper facilitates the researchers to understand the problems, challenges and issues related to this technology. Shweta S. Darekar | Dr. Anandhi Giri ""Problems in Autonomous Driving System of Smart Cities in IoT"" Published in International Journal of Trend in Scientific Research and Development (ijtsrd), ISSN: 2456-6470, Volume-4 | Issue-2 , February 2020,
URL: https://www.ijtsrd.com/papers/ijtsrd30079.pdf
Paper Url : https://www.ijtsrd.com/computer-science/other/30079/problems-in-autonomous-driving-system-of-smart-cities-in-iot/shweta-s-darekar
The development of driverless vehicles is fast, and the technology has the potential to significantly affect the transport system, society and environment
The Autonomous car is about to enter the mass-market. The question is not about when it will happen but in which conditions, under which form or who will be the first car manufacturer to release an efficient and reliable final product. Entirely unexpected ways to deal with building up the AI frameworks for self-driving vehicles exist and most of them are horribly best in class and with extremely high equipment needs. The appropriate response presented during this paper proposes the AI fundamentally based framework to be as simple as conceivable with low equipment needs. A straight forward three layers profound, totally associated neural system was prepared to outline pictures from a forward looking QVGA camera to directing orders. Upheld an information picture the neural system should settle on one among the four offered orders (Forward, Left, Right or Stop). With least of the instructing information (250 pictures) the framework figured out how to follow the street ahead and keep in its path.The framework precisely learns essential street alternatives with exclusively the directing point in light of the fact that the contribution from the human driver. it had been near explicitly prepared to watch lines out and about. Contrasted with rather progressively confounded arrangements like express decay of the issue, similar to path identification and the board and convolutional neural systems simply like the conclusion to complete the process of learning arranged by the N-Vidia this technique demonstrated to be amazingly solid and affordable. we will in general attempt to demonstrate that this methodology would bring about better and lower equipment necessities so making the occasion of oneself driving vehicles simpler and more financially savvy. Simple counterfeit neural system, much the same as the one gave during this paper, is sufficient for relatively muddled technique like path keeping.
The Art of the Pitch: WordPress Relationships and SalesLaura Byrne
Clients don’t know what they don’t know. What web solutions are right for them? How does WordPress come into the picture? How do you make sure you understand scope and timeline? What do you do if sometime changes?
All these questions and more will be explored as we talk about matching clients’ needs with what your agency offers without pulling teeth or pulling your hair out. Practical tips, and strategies for successful relationship building that leads to closing the deal.
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
LF Energy Webinar: Electrical Grid Modelling and Simulation Through PowSyBl -...DanBrown980551
Do you want to learn how to model and simulate an electrical network from scratch in under an hour?
Then welcome to this PowSyBl workshop, hosted by Rte, the French Transmission System Operator (TSO)!
During the webinar, you will discover the PowSyBl ecosystem as well as handle and study an electrical network through an interactive Python notebook.
PowSyBl is an open source project hosted by LF Energy, which offers a comprehensive set of features for electrical grid modelling and simulation. Among other advanced features, PowSyBl provides:
- A fully editable and extendable library for grid component modelling;
- Visualization tools to display your network;
- Grid simulation tools, such as power flows, security analyses (with or without remedial actions) and sensitivity analyses;
The framework is mostly written in Java, with a Python binding so that Python developers can access PowSyBl functionalities as well.
What you will learn during the webinar:
- For beginners: discover PowSyBl's functionalities through a quick general presentation and the notebook, without needing any expert coding skills;
- For advanced developers: master the skills to efficiently apply PowSyBl functionalities to your real-world scenarios.
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.
Observability Concepts EVERY Developer Should Know -- DeveloperWeek Europe.pdfPaige Cruz
Monitoring and observability aren’t traditionally found in software curriculums and many of us cobble this knowledge together from whatever vendor or ecosystem we were first introduced to and whatever is a part of your current company’s observability stack.
While the dev and ops silo continues to crumble….many organizations still relegate monitoring & observability as the purview of ops, infra and SRE teams. This is a mistake - achieving a highly observable system requires collaboration up and down the stack.
I, a former op, would like to extend an invitation to all application developers to join the observability party will share these foundational concepts to build on:
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.
Generative AI Deep Dive: Advancing from Proof of Concept to ProductionAggregage
Join Maher Hanafi, VP of Engineering at Betterworks, in this new session where he'll share a practical framework to transform Gen AI prototypes into impactful products! He'll delve into the complexities of data collection and management, model selection and optimization, and ensuring security, scalability, and responsible use.
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.
SAP Sapphire 2024 - ASUG301 building better apps with SAP Fiori.pdfPeter Spielvogel
Building better applications for business users with SAP Fiori.
• What is SAP Fiori and why it matters to you
• How a better user experience drives measurable business benefits
• How to get started with SAP Fiori today
• How SAP Fiori elements accelerates application development
• How SAP Build Code includes SAP Fiori tools and other generative artificial intelligence capabilities
• How SAP Fiori paves the way for using AI in SAP apps
Securing your Kubernetes cluster_ a step-by-step guide to success !KatiaHIMEUR1
Today, after several years of existence, an extremely active community and an ultra-dynamic ecosystem, Kubernetes has established itself as the de facto standard in container orchestration. Thanks to a wide range of managed services, it has never been so easy to set up a ready-to-use Kubernetes cluster.
However, this ease of use means that the subject of security in Kubernetes is often left for later, or even neglected. This exposes companies to significant risks.
In this talk, I'll show you step-by-step how to secure your Kubernetes cluster for greater peace of mind and reliability.
2. Introduction
In this presentation we will discuss different approaches to
Autonomous Driving, published by a variety of research
institutes from around the world.
The main topics covered in the following publications are the
technical aspects of allowing autonomous driving, as well as
legislation of autonomous driving systems, and the theory and
algorithms behind the technology.
2
3. (1) How Google‘s Self Driving Car Works
The article speaks on, literally, how Google‘s self-driving car
works, the benefits of a self-driving car and the approach
Google‘s engineers take in order to solve different challenges.
3
4. (1) How Google‘s Self Driving Car Works
• Different sensoring systems are being used other than the
GPS in order for the car to understand the environment
better
• The car is already sensative enough to recognize
pedastrians in a very short time
4
5. (2) Driverless car travelling guide
system
• A patent registered in the US in 1989 by Yoshihiro Saitoh,
Minoru Kondoh and Yukio Komatsu
• The invention is aimed to help driverless cars drive on pure
electric force in a closed travel-path
• The system is splitted to car-side and ground-side, where the
car side of the system follows an exact travel-path on the groud
(like Straßenbahnen travel)
• The car is driven by a magnetic field created by an electric
current in the guide line in the ground
5
6. (2) Driverless car travelling guide
system
• A bus that works in a similar way, is
nowadays operative in KAIST
University in S. Korea
• Power strips are buried 30 cm below
the ground, connected to the national
grid
• Pick-up equipment underneath the the vehicle collects power
through non-contact
magnetic induction
which is used
either to power the
vehicle prime-mover
or for battery
charging.
6
7. (3) The Pathway to Driverless cars: A
Code of Practice for Testing
• A legal document issued by the Department for Transport in the
British government (2015)
• The document is meant to facilitate the development of
autonomous driving technologies, acknowledging the potential
benefits, such as safety and reducing casualties
• Topics covered: Road traffic, insurance, Engagement of testers
in driving, requirements from testers and operators of the cars,
vehicle requirements, data security, failure warnings and
software requirements
7
8. (4) The CyberCars
• A program done by 15 European research institutes and private
industrial companies
• The program objective is a new intelligent Transportation based
on automated-driving cars
• Started with the concept of car-sharing: A fleet of cars used by a
large number of users
• The main notion is that we would be able to
call the car when needed and would be also
be able to park it far away from the city, giving
more space for pedestrians and cyclists
8
9. (4) The CyberCars
• Benefits of CyberCars mentioned in the article are:
- Reduction of congestion in the city, flowing traffic
- Better air quality and energy conservation
- Increased safety
- Can be moved easily between locations, and remote parking
- Delivery of goods or even garbage collection
- Long-term optimization of system performance
• Technology that would be use consists both the car‘s side and
the infrastructure’s side. They are to be based on cutting-edge
technologies as well as new technologies that should be
invented 9
10. (5) Combining 3D Shape, Color, and
Motion for Robust Anytime Tracking
• An article from Stanford University using mathematical methods
and algorithms to better detect objects on real life scenarios
• Object tracking has been studied for decades, but tracking
algorithms suffer from low accuracy and low robustness in real-
world data
• The authors suggest a tracker that combines 3D shape, color
(when available) and motion cues to accurately detecting
moving objects on real time 10
11. (6) Group Induction
• Group Induction is a suggested mathematical framework that is
aimed at improving object recognition
• It allows a much better machine-learning methods for
perception-systems than the methods that exist today.
• The usefulness of better object
recognition that this method
suggests can be seen when it is
implemented in autonomous
vehicles 11
12. (6.5) Tracking-Based Semi-
Supervised Learning
• The previous paper was based on this one, published by the
same authors
• A machine learning method, can be used by robotic systems to
learn how to recognize new 3D objects constantly
• The algorithm used is faster than methods known today by a
factor of three, and requires less user-annotated data
12
13. (7) Autonomes Fahren – Erkenntnisse
aus der DARPA Urban Challenge
• Die DARPA Urban Challenge 2007 ist ein Rennen zwischen
autonomen Roboterfahrzeugen, das auf der ehemaligen George
Air Force Base stattgefunden hat
• Der Versuchsträger Caroline war das Auto der Universität
Braunschweig und war das beste nicht-amerikanische Team,
dass den siebten Platz erreicht
• Der Artikel spricht über lessons learned aus der Erfahrung und
wirft Fragen auf, und auch er spricht über die Zukunft der
Autonomen Fahren.
13
14. (8) Towards Fully Autonomous
Driving: Systems and Algorithms
• This paper mentions all the main improvements made in Junior,
the Stanford autonomous vehicle, that were made since the
DARPA Urban Challenge in 2007 (issued in 2011)
• It includes improvements in Hardware (including the car itself),
Software, laser calibaration, mapping and localization, object
recognition, trajectory planning and algorithms for modelling
real-world scenarions.
14
15. (9) Are we ready for Autonomous Driving?
The KITTI Vision Benchmark Suite
• Annieway, KIT‘s autonomous vehicle also participated in DARPA
Urban Challenge in 2007
• The authors use the autonomous driving platform in order to
develop novel challenging benchmarks for autonomous driving
systems
• The platform has been checked in the streets of Karlsruhe, rural
areas and high-ways
15
16. (10) AUTOMATIC LASER CALIBRATION,MAPPING,
AND LOCALIZATIONFOR AUTONOMOUS VEHICLES
• This dissertation presents several related algorithms that enable
important capabilities for self-driving vehicles
• Different algorithms are presented for laser calibration,
mapping and localizations
• A combination of computer-vision techniques and probabilistic
approaches to incorporating uncertainty
16
- Presentation talks about different research article
- Articles talk about: technical aspects, legislation, theory and algorithms of technology.
www.123seminarsonly.com/Seminar-Reports/2015-03/190666282-Google-Car.docx Published by Erico Guizzo The Google blog about Autonomous vehicles: http://googleblog.blogspot.co.at/2010/10/what-were-driving-at.html The TED lecture about Google’s self-driving cars: http://www.ted.com/talks/sebastian_thrun_google_s_driverless_car
www.123seminarsonly.com/Seminar-Reports/2015-03/190666282-Google-Car.docx Google engineers are driving with the car in an open Environment at least once.
Over 300,000 km has been driven with Google’s automatic cars.
The main goals mentioned in the article are: Optimization of fuel, road space and parking, minimizing accidents and risks in the road, and sharing cars instead of owning them.
https://www.google.com/patents/US4855656
More info can be found in the wikipedia page: https://en.wikipedia.org/wiki/Online_Electric_Vehicle *The bus is acting as a shuttle in the campus area for students for free *I drove in it; works perfectly
https://www.gov.uk/government/uploads/system/uploads/attachment_data/file/446316/pathway-driverless-cars.pdf *manual driving should always be available
http://www.researchgate.net/publication/228884497_The_CyberCars Published by Georges Gallais, Michel Parent
INRIA
*The picture on the upper-right displays a remote-controlled vehicle
Car sharing is increasingly used, especially in Germany and Switzerland, however, to date, a door-to-door service is yet to be available for users
http://www.researchgate.net/publication/228884497_The_CyberCars Published by Georges Gallais, Michel Parent
INRIA
When optimizing system performance, the private consumer‘s needs will be taken into account, as well as public requirements The system will work, or will learn to work in different modes and times in the day, week and year. The technologies mentioned for the car‘s side are: self-navigation and guidance, object detection and collision avoidance and platooning Technologies on the infrastructure side: resource-management, user-interface, compatible information systems and remote control on vehicles
http://www.roboticsproceedings.org/rss10/p14.pdf
Published by David Held, Jesse Levinson, Sebastian Thrun, Silvio Savarese The main goal of the tracker is autonomous driving systems. One of the main examples brought to the usefulness of the tracker is a that a self-driving car should recognize when in a line of parking cars, one of them is going out.
The 3D shape is put in a probabilistic framework, where cues of shape, motion and color are also being combined, in such a way that tracking accuracy is increased over time. At the beginning, posterior information is being used.
This article is in a higher level of mathematics.
http://cs.stanford.edu/people/teichman/papers/iros2013-group_induction.pdf
Published by Alex Teichman and Sebastian Thrun
Stanford University
- Perception systems today require thousands or ten-of-thousands training examples in order learn their environment. Usually it takes a lot of user-annotated data which is time-consuming, expensive and difficult to collect.
- The mathematics here suggest several improvements to the heuristic methods: It is generic and can be used any scenario where unlabled data has a group structure Can be used for autonomous driving Requires only tens of training examples The picture on the upper-right side is Junior, Stanford‘s self-driving car, used by the writers to evalute the Group Induction for object recognition. This vehicle won the second place on the DARPA Uban Challenge (explained in the next slide) The examples on the lower-right side depict object recognition the paper is written with a high-level of mathematics
http://cs.stanford.edu/people/teichman/papers/rss2011.pdf
Published by Alex Teichman, Sebastian Thrun
Stanford University A machine learning classifier which correctly recognizes several frames of a track of a bicyclist can infer that the remaining frames also are of a bicyclist. This enables the addition of new, useful training examples that include changes in pose (as above), occlusion level, and viewing distance.
http://www.degruyter.com/view/j/itit.2008.50.issue-4/itit.2008.0493/itit.2008.0493.xml Herausgegeben von Christian Berger und Bernhard Rumpe, Universität Braunschweig
http://cs.stanford.edu/people/teichman/papers/iv2011.pdf
Published by many people (Can be seen in the link above), 2011 IEEE Intelligent Vehicles Symposium The DARPA challenge was the closest trial of real-world autonomous driving, however, it had a few disadvantages as a represantation of the real-world: - It was closed to pedastrians and bicyclists
Speed limit was 35 MPH (56.3 KMH) - There were no traffic lights
DARPA officials were allowed to pause, interrupt, and restart an individual vehicle, in order to minimize risk and allow smoother operation
Therefore, things such as traffic light detection were added later and can be read about in the article
http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=6248074&tag=1 Published by Andreas Geiger and Philip Lenz, Karlsruhe Institute of Technology, and by Raquel Urtasun, Toyota Technological Institute at Chicago
Benchmarks are developped for optical flow, visual Odometry and 3D object detection
A video about the KIT Annieway car: http://www.cvlibs.net/datasets/kitti/
https://stacks.stanford.edu/file/druid:zx701jr9713/JesseThesisFinal2-augmented.pdf Published by Jesse Sol Levinson, Stanford University
Handed as a PhD thesis in August 2011 The pictures above depict how the car uses a 64 beam laser to `discover` the environment arround it
