Dedicated roads for autonomous vehicles Jeffrey Funk
These slides use concepts from my (Jeff Funk) course entitled analyzing hi-tech opportunities to analyze how autonomous vehicles are becoming economic feasible. They are becoming economically feasible because the cost of lasers, ICs, MEMS, and other electronic components are falling at 25 to 40% per year. If the cost of autonomous vehicles fall 25% a year, the cost of the electronics associated with autonomous vehicles will fall 90% in 10 years. Dedicating roads to autonomous vehicles is necessary to achieve the most benefits from autonomous vehicles. While using autonomous vehicles in combination with conventional vehicles can free drivers for other activities, dedicating roads to autonomous vehicles can dramatically reduce congestion, increase speeds, and thus increase the number of cars per area of the road. They can also reduce accidents, insurance, and the number of traffic police. These slide discuss a number of technologies that can be used for the dedicated roads including wireless communication, magnetic stripes and RFIDs that together can coordinate vehicles on roads. The slides end by summarizing efforts in Singapore.
Adrian Pearmine of DKS Associates presented at Drive Oregon's October 2015 event. He highlighted new modes of mobility that are anticipated to transform our transportation system and discussed best practices for private and municipal planners to use when planning for these changes.
Autonomous vehicles: becoming economically feasible through improvements in l...Jeffrey Funk
These slides use concepts from my (Jeff Funk) course entitled analyzing hi-tech opportunities to analyze how autonomous vehicles are becoming economically feasible throug through improvements in lasers, microelectronic mechanical systems (MEMS), integrated circuits (ICs), and other components. Although the cost of the Google Car is currently about 150,000 USD, 30% annual improvements in lasers, MEMS, and ICs will make these economically feasible for a broad number of users in the next ten years. A key issue is when certain lanes, roads or even entire highway systems are restricted to automated vehicles. This would enable collision avoidance to rely more on between-vehicle communications. This would further reduce the cost of automated vehicles, stimulate diffusion, and also reduce transportation time and increase fuel efficiency.
Smart infrastructure for autonomous vehicles Jeffrey Funk
These slides use concepts from my (Jeff Funk) course entitled analyzing hi-tech opportunities to analyze how autonomous vehicles are becoming economic feasible. They are becoming economically feasible because the cost of lasers, ICs, MEMS, and other electronic components are falling at 25 to 40% per year. If the cost of autonomous vehicles fall 25% a year, the cost of the electronics associated with autonomous vehicles will fall 90% in 10 years. Dedicating roads to autonomous vehicles is necessary to achieve the most benefits from autonomous vehicles. While using autonomous vehicles in combination with conventional vehicles can free drivers for other activities, dedicating roads to autonomous vehicles can dramatically reduce congestion, increase speeds, and thus increase the number of cars per area of the road. They can also reduce accidents, insurance, and the number of traffic police. These slide discuss the use of wireless technologies for the control and coordination of autonomous vehicles. Improvements in bandwidth, speed, and latency (delays) along with improvements in computer processing are occurring and these improvements are making dedicated roads for autonomous vehicles economically feasible.
2017 Autonomous Vehicle Presentation Package Michael Scheno
This exclusive package includes presentations by Annabel R. Chang, Director of Public Policy at Lyft, Glen DeVos, Vice President – Engineering at Delphi, and Sam Abuelsamid, Senior Research Analyst at Navigant Research.
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.
Just what is that thing on top of the Google Car? What does adaptive cruise control with lane assist mean? When are these things going to be ready? The answer to these questions and more in a technology overview that unravels just how these vehicles are going to work. Presented at the 2017 D-STOP Symposium.
Dedicated roads for autonomous vehicles Jeffrey Funk
These slides use concepts from my (Jeff Funk) course entitled analyzing hi-tech opportunities to analyze how autonomous vehicles are becoming economic feasible. They are becoming economically feasible because the cost of lasers, ICs, MEMS, and other electronic components are falling at 25 to 40% per year. If the cost of autonomous vehicles fall 25% a year, the cost of the electronics associated with autonomous vehicles will fall 90% in 10 years. Dedicating roads to autonomous vehicles is necessary to achieve the most benefits from autonomous vehicles. While using autonomous vehicles in combination with conventional vehicles can free drivers for other activities, dedicating roads to autonomous vehicles can dramatically reduce congestion, increase speeds, and thus increase the number of cars per area of the road. They can also reduce accidents, insurance, and the number of traffic police. These slide discuss a number of technologies that can be used for the dedicated roads including wireless communication, magnetic stripes and RFIDs that together can coordinate vehicles on roads. The slides end by summarizing efforts in Singapore.
Adrian Pearmine of DKS Associates presented at Drive Oregon's October 2015 event. He highlighted new modes of mobility that are anticipated to transform our transportation system and discussed best practices for private and municipal planners to use when planning for these changes.
Autonomous vehicles: becoming economically feasible through improvements in l...Jeffrey Funk
These slides use concepts from my (Jeff Funk) course entitled analyzing hi-tech opportunities to analyze how autonomous vehicles are becoming economically feasible throug through improvements in lasers, microelectronic mechanical systems (MEMS), integrated circuits (ICs), and other components. Although the cost of the Google Car is currently about 150,000 USD, 30% annual improvements in lasers, MEMS, and ICs will make these economically feasible for a broad number of users in the next ten years. A key issue is when certain lanes, roads or even entire highway systems are restricted to automated vehicles. This would enable collision avoidance to rely more on between-vehicle communications. This would further reduce the cost of automated vehicles, stimulate diffusion, and also reduce transportation time and increase fuel efficiency.
Smart infrastructure for autonomous vehicles Jeffrey Funk
These slides use concepts from my (Jeff Funk) course entitled analyzing hi-tech opportunities to analyze how autonomous vehicles are becoming economic feasible. They are becoming economically feasible because the cost of lasers, ICs, MEMS, and other electronic components are falling at 25 to 40% per year. If the cost of autonomous vehicles fall 25% a year, the cost of the electronics associated with autonomous vehicles will fall 90% in 10 years. Dedicating roads to autonomous vehicles is necessary to achieve the most benefits from autonomous vehicles. While using autonomous vehicles in combination with conventional vehicles can free drivers for other activities, dedicating roads to autonomous vehicles can dramatically reduce congestion, increase speeds, and thus increase the number of cars per area of the road. They can also reduce accidents, insurance, and the number of traffic police. These slide discuss the use of wireless technologies for the control and coordination of autonomous vehicles. Improvements in bandwidth, speed, and latency (delays) along with improvements in computer processing are occurring and these improvements are making dedicated roads for autonomous vehicles economically feasible.
2017 Autonomous Vehicle Presentation Package Michael Scheno
This exclusive package includes presentations by Annabel R. Chang, Director of Public Policy at Lyft, Glen DeVos, Vice President – Engineering at Delphi, and Sam Abuelsamid, Senior Research Analyst at Navigant Research.
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.
Just what is that thing on top of the Google Car? What does adaptive cruise control with lane assist mean? When are these things going to be ready? The answer to these questions and more in a technology overview that unravels just how these vehicles are going to work. Presented at the 2017 D-STOP Symposium.
This is a presentation that focuses on autonomous vehicles technology. The presentation describes key sensor technologies integrated under the bonnet of a driverless car. After a brief introduction, the presentation dwells deeper into each sensor technology demonstrating examples of self driving cars such as Google's self driving car, DARPA URBAN challenge etc., along the way. It also introduces the concept of electronic control units which is responsible for collecting data from different sensors and respond to other units accordingly. The slides also build a platform for vehicle to vehicle communication technology, types and its application areas.
Roadmap to autonomous driving, AV levels, its impact
on powertrains of the future
- Autonomus driving vehicle
- Powertrain requirements for autonomous vehicles
- Scaleable functionality for ACE (Autonomous, Connected and Electric)
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
This presentation was made by Phil Carter of ARUP, at the Shared and App Based Transport Innovation seminar, organised by the Institute for Sensible Transport.
Designing Roads for AVs (autonomous vehicles)Jeffrey Funk
Autonomous vehicles (AVs) represent one of the most promising new technologies for smart cities and for humans in general. The problem is that cities will not realize the full benefits from AVs until roads are designed for them. Until this occurs, their main benefit will be the elimination of the driver and steering wheel, which will reduce the cost and increase the capacity of taxis; but even this impact will not occur for many years because of safety concerns. Thus, in the near term, the main benefit of AVs will be free time for the driver to do emails and other smart phone related tasks.
A better solution is to design roads for AVs or in other words, to constrain the environment for AVs in order to simplify the engineering problem for them. For example, designing roads so that all vehicles can be controlled by a combination of wireless communication, RFID tags, and magnets will reduce the cost of AVs and increase their benefits. Only AVs would be allowed on these roads, they are checked for autonomous capability at the entrance, and control is returned to the driver when an AV leaves the road. Existing cars can be retrofitted with wireless modules that enable cars to be controlled by a central system, thus enabling cars to travel closely together. The magnets and RFID tags create an invisible railway that keeps the AVs in their lanes while wireless communication is used for lane changing and exiting a highway (Chang et al, 2014; Le Quesne et al, 2014). These wireless modules, magnets and RFID tags will be much cheaper than the expensive LIDAR that is needed when AVs are mixed with conventional vehicles on a road.
The benefits from dedicating roads to AVs include higher vehicle densities, less congestion, faster travel times, and higher fuel efficiencies. These seemingly contradicting goals can be achieved because AVs can have shorter inter-vehicle distances even at high speeds thus enabling higher densities, lower congestion, and lower travel times. The less congestion and thus fewer instances of slow moving or stopped vehicles enable the vehicles to travel at those speeds at which higher fuel efficiencies can be achieved (Funk, 2015). In combination with new forms of multiple passenger ride sharing, the higher fuel efficiencies will also reduce carbon emissions and thus help fight climate change.
The challenge is to develop a robust system that can be easily deployed in various cities and that will be compatible with vehicles containing the proper subsystems. Such a system can be developed in much the same way that new cellular systems are developed and tested. Suppliers of mobile phone infrastructure, automobiles, sensors, LIDAR, 3D vision systems, and other components must work with city governments and universities to develop and test a robust architecture followed by the development of a detail design.
Advanced driver assistance systems are designed to increase car safety more generally road safety.
Basically Advanced driver assists(ADS) systems helps the driver in the driving process and enables safe, relaxed driving. It makes sense to get your new car with driver assist features if you find it at a reasonable price as it helps you drive easily and safely in everyday use.
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.
Research presentation on Autonomous Driving. Direction perception approach.
Research work by Princeton University group.
Note: Link given in the presentation
Imaging Technologies for Automotive 2016 Report by Yole Developpement Yole Developpement
Imaging technology, which is currently mainly cameras, is exploding into the automotive space, and is set to grow at 20% CAGR to reach $7.3B in 2021
INFOTAINMENT AND ADVANCED DRIVER ASSISTANCE SYSTEMS (ADAS) PROPEL AUTOMOTIVE IMAGING
Since 2008, when a recession acted as a wakeup call to the whole industry, the automotive market has undergone obvious structural change. Capitalizing on technologies initially developed for smartphones, electronics have invaded, and imaging technology is now taking center stage. From less than one camera per car on average in 2015, there will be more than three cameras per car by 2021, which means 371 million automotive imaging devices.
Cameras were initially mounted for ADAS purposes on high-end vehicles, with deep learning image analysis techniques promoting early adoption. The Israeli company Mobileye has been instrumental in bringing this technology to market, along with On Semiconductor, which provided the CMOS image sensor. Copycat competition will probably pick up as the market now justifies initial investment in design and technology. It is now a well-established fact that vision-based autonomous emergency braking (AEB) is possible and saves life. Adoption of forward ADAS cameras will therefore accelerate.
Growth of imaging for automotive is also being fueled by the park assist application, and 360° surround view camera volume is skyrocketing. While it’s becoming mandatory in the US to have a rearview camera, that uptake is dwarfed by 360° surround view cameras, which enable a “bird’s eye view” perspective. This trend is most beneficial to companies like Omnivision at sensor level and Panasonic and Valeo, which have become the main manufacturers of automotive cameras.
Mirror replacement cameras are currently the big unknown and take-off will primarily depend on its appeal and car design regulation. Europe and Japan are at the forefront of this trend, which should become slightly significant by 2021.
Solid state lidar is well talked about and will start to be found in high end cars by 2021. Cost reduction will be a key driver as the push for semi-autonomous driving will be felt more strongly by car manufacturers. The report will analyse the impact of lidar for automotive vision in detail.
Night vision cameras using Long Wave Infrared (LWIR) technology were initially perceived as a status symbol. However, they’re increasingly appreciated for their ability to automatically detect pedestrians and wildlife. LWIR will therefore become integrated into ADAS systems in future.
3D cameras will be limited to in-cabin infotainment and driver monitoring. This technology will be key for luxury cars and therefore is of limited use today.
If any significant semi-autonomous trend picks up, the technology will probably become mandatory, due to safety issues.
More information on that report at http://www.i-micronews.com/reports.html
Sensors and Data Management for Autonomous Vehicles report 2015 by Yole Devel...Yole Developpement
Multiple sensing technologies will ensure many market opportunities for Tier 1 players, Tier 2 players, and newcomers alike
Sensor technologies are a driving force in making fully autonomous vehicles a reality. Automakers are racing to develop safe self-driving cars, but this race is a distance run more than a sprint, where multiple automation stages will imply multiple sensors. Ultrasonic sensors, radars, and multiple cameras systems are already embedded in high-end vehicles -- and within 10 years, they could also include long-range cameras, LIDAR, micro bolometer and accurate dead reckoning. These devices will work concurrently and each technology will support another to ensure codependency and avoid concerns. Even though sensors are only part of the puzzle, their market opportunities are promising.
This is a presentation that focuses on autonomous vehicles technology. The presentation describes key sensor technologies integrated under the bonnet of a driverless car. After a brief introduction, the presentation dwells deeper into each sensor technology demonstrating examples of self driving cars such as Google's self driving car, DARPA URBAN challenge etc., along the way. It also introduces the concept of electronic control units which is responsible for collecting data from different sensors and respond to other units accordingly. The slides also build a platform for vehicle to vehicle communication technology, types and its application areas.
Roadmap to autonomous driving, AV levels, its impact
on powertrains of the future
- Autonomus driving vehicle
- Powertrain requirements for autonomous vehicles
- Scaleable functionality for ACE (Autonomous, Connected and Electric)
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
This presentation was made by Phil Carter of ARUP, at the Shared and App Based Transport Innovation seminar, organised by the Institute for Sensible Transport.
Designing Roads for AVs (autonomous vehicles)Jeffrey Funk
Autonomous vehicles (AVs) represent one of the most promising new technologies for smart cities and for humans in general. The problem is that cities will not realize the full benefits from AVs until roads are designed for them. Until this occurs, their main benefit will be the elimination of the driver and steering wheel, which will reduce the cost and increase the capacity of taxis; but even this impact will not occur for many years because of safety concerns. Thus, in the near term, the main benefit of AVs will be free time for the driver to do emails and other smart phone related tasks.
A better solution is to design roads for AVs or in other words, to constrain the environment for AVs in order to simplify the engineering problem for them. For example, designing roads so that all vehicles can be controlled by a combination of wireless communication, RFID tags, and magnets will reduce the cost of AVs and increase their benefits. Only AVs would be allowed on these roads, they are checked for autonomous capability at the entrance, and control is returned to the driver when an AV leaves the road. Existing cars can be retrofitted with wireless modules that enable cars to be controlled by a central system, thus enabling cars to travel closely together. The magnets and RFID tags create an invisible railway that keeps the AVs in their lanes while wireless communication is used for lane changing and exiting a highway (Chang et al, 2014; Le Quesne et al, 2014). These wireless modules, magnets and RFID tags will be much cheaper than the expensive LIDAR that is needed when AVs are mixed with conventional vehicles on a road.
The benefits from dedicating roads to AVs include higher vehicle densities, less congestion, faster travel times, and higher fuel efficiencies. These seemingly contradicting goals can be achieved because AVs can have shorter inter-vehicle distances even at high speeds thus enabling higher densities, lower congestion, and lower travel times. The less congestion and thus fewer instances of slow moving or stopped vehicles enable the vehicles to travel at those speeds at which higher fuel efficiencies can be achieved (Funk, 2015). In combination with new forms of multiple passenger ride sharing, the higher fuel efficiencies will also reduce carbon emissions and thus help fight climate change.
The challenge is to develop a robust system that can be easily deployed in various cities and that will be compatible with vehicles containing the proper subsystems. Such a system can be developed in much the same way that new cellular systems are developed and tested. Suppliers of mobile phone infrastructure, automobiles, sensors, LIDAR, 3D vision systems, and other components must work with city governments and universities to develop and test a robust architecture followed by the development of a detail design.
Advanced driver assistance systems are designed to increase car safety more generally road safety.
Basically Advanced driver assists(ADS) systems helps the driver in the driving process and enables safe, relaxed driving. It makes sense to get your new car with driver assist features if you find it at a reasonable price as it helps you drive easily and safely in everyday use.
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.
Research presentation on Autonomous Driving. Direction perception approach.
Research work by Princeton University group.
Note: Link given in the presentation
Imaging Technologies for Automotive 2016 Report by Yole Developpement Yole Developpement
Imaging technology, which is currently mainly cameras, is exploding into the automotive space, and is set to grow at 20% CAGR to reach $7.3B in 2021
INFOTAINMENT AND ADVANCED DRIVER ASSISTANCE SYSTEMS (ADAS) PROPEL AUTOMOTIVE IMAGING
Since 2008, when a recession acted as a wakeup call to the whole industry, the automotive market has undergone obvious structural change. Capitalizing on technologies initially developed for smartphones, electronics have invaded, and imaging technology is now taking center stage. From less than one camera per car on average in 2015, there will be more than three cameras per car by 2021, which means 371 million automotive imaging devices.
Cameras were initially mounted for ADAS purposes on high-end vehicles, with deep learning image analysis techniques promoting early adoption. The Israeli company Mobileye has been instrumental in bringing this technology to market, along with On Semiconductor, which provided the CMOS image sensor. Copycat competition will probably pick up as the market now justifies initial investment in design and technology. It is now a well-established fact that vision-based autonomous emergency braking (AEB) is possible and saves life. Adoption of forward ADAS cameras will therefore accelerate.
Growth of imaging for automotive is also being fueled by the park assist application, and 360° surround view camera volume is skyrocketing. While it’s becoming mandatory in the US to have a rearview camera, that uptake is dwarfed by 360° surround view cameras, which enable a “bird’s eye view” perspective. This trend is most beneficial to companies like Omnivision at sensor level and Panasonic and Valeo, which have become the main manufacturers of automotive cameras.
Mirror replacement cameras are currently the big unknown and take-off will primarily depend on its appeal and car design regulation. Europe and Japan are at the forefront of this trend, which should become slightly significant by 2021.
Solid state lidar is well talked about and will start to be found in high end cars by 2021. Cost reduction will be a key driver as the push for semi-autonomous driving will be felt more strongly by car manufacturers. The report will analyse the impact of lidar for automotive vision in detail.
Night vision cameras using Long Wave Infrared (LWIR) technology were initially perceived as a status symbol. However, they’re increasingly appreciated for their ability to automatically detect pedestrians and wildlife. LWIR will therefore become integrated into ADAS systems in future.
3D cameras will be limited to in-cabin infotainment and driver monitoring. This technology will be key for luxury cars and therefore is of limited use today.
If any significant semi-autonomous trend picks up, the technology will probably become mandatory, due to safety issues.
More information on that report at http://www.i-micronews.com/reports.html
Sensors and Data Management for Autonomous Vehicles report 2015 by Yole Devel...Yole Developpement
Multiple sensing technologies will ensure many market opportunities for Tier 1 players, Tier 2 players, and newcomers alike
Sensor technologies are a driving force in making fully autonomous vehicles a reality. Automakers are racing to develop safe self-driving cars, but this race is a distance run more than a sprint, where multiple automation stages will imply multiple sensors. Ultrasonic sensors, radars, and multiple cameras systems are already embedded in high-end vehicles -- and within 10 years, they could also include long-range cameras, LIDAR, micro bolometer and accurate dead reckoning. These devices will work concurrently and each technology will support another to ensure codependency and avoid concerns. Even though sensors are only part of the puzzle, their market opportunities are promising.
Localization in V2X Communication NetworksStefano Severi
Presentation made by Alireza Ghods and given by Dr. Stefano Severi at CCP Workshop co-located with IEEE Intelligent Vehicles Conference, 19th June 2016 Gothenburg (Sweden)
Talk from /dev/summer
Brief overview of Simulatneous Localistion and Mapping incl. brief intro to localisation methods. Relates these methods to autonomous vehicles and touches on ethical concerns.
How do we prepare for the next 40 years? Do we need to worry about this now? What do we know about the timeline? We will explore what we know now and what we need to consider going forward. Presented at the 2017 D-STOP Symposium.
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
Global Advanced Driver Assistance Systems (ADAS) Market: Trends and Opportuni...Daedal Research
The report titled “Global Advanced Driver Assistance Systems (ADAS) Market: Trends and Opportunities (2013-2018)” provides an in-depth analysis of global advanced driver assistance system market. For more mail me: info@daedal-research.com
Delphi Integrated Radar and Camera System (RACam) 2016 teardown reverse costi...Yole Developpement
Delphi is the first to provide a single system combining 76Ghz Radar and Vision Sensing. With a compact design the system can be integrated behind the windshield and enables a broad array of active safety features, as lane tracking, collision avoidance or adaptive cruise control.
The visual detection is performed by a 1/3” CMOS Image Sensor supplied by a leader in the CIS automotive industry. The sensor is surmounted by a specific 7-lens module and the Mobileye EyeQ3 SoC is used for video processing. Concerning the radar function, Receiver and Transmitter chips from Infineon using SiGe HBT technology are assembled by wire bonding on the RF Board. The Antenna board uses a PTFE-based substrate and is equipped with planar antennas for transmission and reception of the RF signals.
Based on a complete teardown analysis of the Delphi RACam, the report provides the bill-of-material (BOM) and the manufacturing cost of the system. The report also includes analysis of the Image Sensor and Lens module. A structural analysis, with a comparison with Bosch MRR1, highlights the technical choices in RF design made by Delphi.
A physical analysis and manufacturing cost estimation of the Infineon RF chips is available in a separate report, which also includes a comparison with MMICs used in the Bosch MRR1 Radar.
More information on that report at http://www.i-micronews.com/reports.html
Autonomous Vehicles: Technologies, Economics, and OpportunitiesJeffrey Funk
These slides use concepts from my (Jeff Funk) course entitled analyzing hi-tech opportunities to show how the cost and performance of autonomous vehicles are improving rapidly. LIDAR, other sensors, ICs, and wireless are experiencing rapid improvements that are enabling the overall cost of AVs to fall. For example, the latency of wireless systems is improving rapidly thus enabling vehicles to be controlled with wireless systems. This is also creating many new opportunities in the vehicle industry in the Internet of Things, data analytics, and logistics. The slides include a detailed discussion of AVs in Singapore, a likely early adopter.
This presentation talks about Software Defined Vehicles, Automotive Standards including Cyber Security and Safety, Agile Methods like SAFe/Less , Continuous Delivery best practices.
Marek Jersak. Autonomous Drive – From Sensors to MotionIT Arena
Marek Jersak, Senior Director, Autonomous Drive Practice at Luxoft Automotive
Autonomous Drive – From Sensors to Motion
Dr. Marek Jersak received his Diploma in Electrical Engineering from Aachen University of Technology, Germany in 1997. From 1997 to 1999 he worked as a compiler design engineer for Conexant Systems in Newport Beach, California. He returned to school in 1999 and graduated with a PhD in Real-Time Embedded System Design from the Technical University of Braunschweig, Germany in 2004. Together with his university fellow Kai Richter, in 2005 Marek co-founded Symtavision GmbH in Braunschweig, and in 2013 Symtavision Inc in Michigan, serving as Managing Director respectively President for those companies. Symtavision became a globally recognized leader in Timing Analysis tools and architecture consulting for automotive real- time systems with a focus on chassis, active safety, powertrain, body-control and in-vehicle networking. In February 2016, Marek and Kai sold Symtavision to Luxoft. Marek became director of the newly formed ‘Under the Hood’ practice inside Luxoft Automotive. The practice grew to more than 200 engineers in 1.5 years. At the end of 2017, we repositioned the practice to focus fully on various levels of automated driving, from Level-2 / 3 mass-production ADAS software to architectures and algorithms for Level-4 and ultimately Level-5 autonomous driving. Marek is now fully focused on building the teams, customer relationships and engagement models that enable a seamless, scalable and agile solutions offering from sensors to actuators, spanning co-development with our customers of system and software architectures, algorithms, automotive-grade software, integration, and testing.
The presentation was used by the Dr. Pratik Desai at his talk at the "Silicon Valley Automotive Open Source" meetup held at HackerDojo on April 7th, 2016.
OpenCar covers OS development for a new market: automotive apps. In-car apps are poised to explode for open source developers. The market is transforming from an inefficient, proprietary model to an HTML5-based “app store” model. To enter and participate in this new target category, developers need access to automakers, automotive systems, and knowledge of industry standards and platforms. http://sdk.opencar.com
Embitel has expertise in developing Android Infotainment Projects, embedded software/hardware developers and infotainment testing. As your infotainment solution partner, we provide end-to-end support throughout your product development roadmap – technology strategy, UX design, infotainment software and hardware development and support.
Embedded Fest 2019. Віталій Нужний. The Mobility Revolution: the Software tha...EmbeddedFest
- The Future of Automotive: Autonomous, Connected, Electric, and Shared
- Unique Challenges on the Automotive Electronics Road
- Evolving from Hardware to Software: Changing to Stay Ahead of the Curve for Tier-1 Businesses
- Auto 2.0: What this Means for Suppliers
A brief introduction to the Eurotech Group and Eurotech’s M2M Field-to-Application Building Blocks for Smart City Applications
M2M Applications and Use Cases: Industrial Air Conditioning System Monitoring, Environmental Monitoring, Retail Shop Performance Measurement, Retail Energy and Asset Management, Elderly Living Project, Taxi Queue Optimization, Parking Management, Cool Chain Monitoring and Fleet Management Optimization
Automated Driving: Innovative Product Development & Safety
1. Automated Driving
Innovative Product Development and Safety
Serge Lambermont
Technical Director Automated Vehicles - Delphi Electronics and Safety
AEM Annual Product Safety and Compliance seminar
21 April 2015
2. Delphi’s global team – at the center of technology
innovation
126
manufacturing
sites
15
major global
technical centers
20,000
engineers
and
scientists
$17.0B
2014 revenue
.... ........
.
..
....
.
.
.
.
.
.
.
..
.
... ..
....
.
..
....
.
....
....
...
..
..
.. ..
..
.
... ....
....
.....
....
..
.
....
..........
...
...
...... .......
......
.
164,000
people in
33
countries
$1.7 B
in
Research &
Development
3. • 1B smartphones sold
worldwide in 2014
• Consumers want
more connectivity in
their vehicles
• Today’s infotainment
products increasingly
designed for internet
connection
Connected infotainment fueling explosive growth
Connected Infotainment $B
3
4. Creating the next generation Infotainment
experience
Connected
navigation
Reconfigurable
cluster & displays
Connected
Infotainment
Eye gaze tracking &
gesture control
2010 2013 Today Tomorrow
World-class user experience converges with Active Safety
5. Active safety past the tipping point
Fatalities per 100M miles
Airbags
Seat belts
Seat belt
mandates
Occupant
detection
Child
seats
Side / curtain
airbags
Energy-
absorbing
bumpers
Active
safety
1965 1985 2005 2025
1
2
3
4
5
6
Active
suspensions
Anti-Lock
Brakes
ESC
5
6. Active safety features provide a range of protection
Electronically
Scanning Radar
Forward
View Camera
RACam
Crash
Sensors
Airbag
Control Unit
Rear View
Camera
Multi Domain
Controller
Rear and Side
Detection System
Fusion of
V2V and V2I
6
7. Convergence:
The catalyst for accelerating growth
2015 2020 20252010
Semi
Automated
Assisted
automation
ADAS
expansion
Fully
Automated
Full cloud
connectivity
Smartphone
integration
Vehicle as a
device
Brought
In connectivity
Convergence into multi domain architectures / controllers
First to market with Multi Domain Controller
7
8. Passenger Car Market
Active Safety
Connectivity AEB TJA Active
Collision
Avoidance
Automated
Highway
Lane change
Passenger Car Market & Commercial Vehicles
primary
Market driven by active safety
NCAP and Connectivity
ADAS Maps
V2x
Functional Safety
Cybersecurity
Production Prototype demosPilot programs / low volume prod
2nd fuel efficient vehicle
ImpactMarket
9. Low Speed Autonomous Market
Active Safety
Connectivity AEB TJA Active
Collision
Avoidance
Automated
Highway
Lane change
Passenger Car Market & Commercial Vehicles
primary
Market driven by active safety
NCAP and Connectivity
ADAS Maps
V2x
Functional Safety
Cybersecurity
Pilot programs
Low Speed
Autonomous
Low Speed Autonomous – no steering wheel
New Market
Localization
Features for localization
Low speed
prototype
pilots
Retirement, airports,
entertainment
work campus,
car free city centers.
Manufacturing..
Production Prototype demosPilot programs / low volume prod
2nd fuel efficient vehicle
ImpactMarket
Personal Rapid Transit
Personal transport
Cargo Scooters
2/3 wheel
Limited width
Fuel efficiency, Individual Mobility
- Sharing of lanes
Car Sharing
Tech enabled car share and rideshare
Cloud based infrastructure
Dynamic digital infrastructure:
- Optimal booking-routing
- Real-time cloud based updates
- New physical infrastructuredual mode –
distribution
New transportation
10. The road to automated driving
Delphi completed first coast-to-coast trip in an automated vehicle
11. Delphi Drive
• Full suite of radar, vision and ADAS
• Automated highway with lane change
• Point-to-point urban autonomous driving
• Multi-domain controller: High-end microprocessor to seamlessly
drive multiple features and functions
• V2V/V2X: Wireless vehicle communication technology extends
the range of existing ADAS functionality
• Intelligent software that enables the vehicle to make complex,
human-like decisions for real-world driving
11
12. Automated Driving Safety Program
Driver Training
• Prior History of Safety
“review driving record” and continuously monitoring
• 3rd Party professional driving training course
Focused on defense driving training and increased awareness
Emergency maneuvers
• Automated Vehicle Training
Technology
Operational Procedures
• Driver Operational Procedures
Driver focussed on driving, “driver always responsible” – readiness to take over
Always second engineer in the car for monitoring vehicle and data
12
17. Essential elements of automated driving
Sensors and
Perception
Computing
Platforms and
Control
Systems
Electrical Architecture
And Network
Management
Vehicle
Connectivity
Off Board (Cloud) Support
and Services
Functional Safety and Security
User Experience
17
18. Multi-Domain Controller maximizes computing
power
Multi-Domain
Controller
+ Scalable software platform
+ Reduced architecture complexity
+ Faster communication
+ Capacity to future proof
First launch in 2016
Centralized vehicle intelligenceMulti-Domain Controller
Radar
Controller
Camera
Controller Airbag
Controll
er
Detection
System
Controller
Crash
Sensor
Controller
Enables future system complexity
19. System design
Sub-system design
Component design
Component
Manufacturing
System test
Sub-system test
Component test
Requirements
decomposition Time
Vehicle-application
design
Vehicle-application
test
Complexity analysis by requirements decomposition
Automated Driving interdependencies between
systems a Structured Approach
20. Component design
Component
Manufacturing
Component test
Time
Product Complexity
Integration
Complexity
System design
Sub-system design
System test
Sub-system test
Vehicle-application
design
Vehicle-application
test
Vehicle Requirement
decomposition
Product Requirement
Decomposition
Integration and Product Complexity drive how
and where development is done
21. A perspective on OEM and supplier interaction
ProductComplexity
Integration Complexity
HighLow
Low
High
MANAGE CHANGE AT CUSTOMER
"DESIGN-IN" INTO VEHICLE SYSTEM
DEFINE THE INTERFACE
MANAGE PERFORMANCE / COST
TRADE-OFF
EXPERT CENTER DESIGN
22. Challenges
• Automated features are available now and will increase in content
• Automated Driving Technology can and does improve safety, and should introduce many
benefits – reducing incidents caused by human error, improving congestion etc.
• Technology is rapidly emerging, but our 100% reliance on it means we have to 100%
understand its behavior
• The already rigorous automotive requirements will continue to add more safety focus
• New modes/use cases for vehicles will emerge and need to be understood from a system
safety perspective
• Standards and regulation – today's safety standards and regulation will require expansion
to cater for full Automated Vehicles
• Cyber security – potential opportunity for malicious threats
• Fully autonomous?