Mercedes-Benz Autonomous Driving / Shazam Project

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Ekrem Şişeci

Mobile

The document discusses Mercedes-Benz's efforts to advance autonomous driving technology beyond highways. It details a project where an S-Class vehicle autonomously drove the original 125-year old Bertha Benz route between Mannheim and Pforzheim with as few human interventions as possible. The goals were to gain experience with autonomous driving in regular traffic conditions and complex scenarios beyond highways using production-level sensors. Key challenges addressed included navigating intersections, dealing with pedestrians and cyclists, and handling unstructured environments. The system architecture and how the vehicle perceives, understands and responds to the driving scenario is also summarized.

Autonomous Driving: The Ideal Experience

tcismenotu

The document discusses research into consumers' ideal experiences with autonomous vehicles. It identifies trustworthiness and personalization as key attributes. Three scenarios are presented: (1) the vehicle maintains safety during distractions, (2) the driver can perform other activities in autonomous mode, and (3) the vehicle drives itself from start to finish. The ideal experience is described as the vehicle driving itself 66% of the time so consumers can relax, be entertained, and be productive instead of focusing on driving.

HYVE autonomous driving report

Michael Bartl

Autonomous Driving (AD) has been said to be the next big disruptive innovation in the years to come. Considered as being predominantly technology driven, it is supposed to have massive societal impact in areas such as insurance, laws and regulations, logistics, automotive industry as well as all types of transportation methods, not only expected to have an enormous environmental and economic effect but also offer the possibility of saving millions of lives worldwide. HYVE Science Labs, in cooperation with the Technical University Hamburg-Harburg and INSIUS have developed the unique worldwide study “Autonomous Driving: The User Perspective” focused on the customer view and acceptance of Autonomous Driving. The study analyses 106,305 comments on Autonomous Driving publicly posted in English on the Internet, finding a more positive than negative attitude towards this new technology in contrast to the most renowned surveys in the field. The focus was placed in the understanding of customer acceptance, a topic that until now under an Autonomous Driving context is limited. While a survey with more than 200 experts on autonomous vehicles by the IEEE (2014), the world's largest professional association for the advancement of technology, defines that the three biggest obstacles to reach the mass adoption of driverless cars are legal liability, policymakers and customer acceptance. Therefore it is essential to start understanding and integrating customers in order to build deep and meaningful customer insights which can be used to deliver the products they want and need. Furthermore it is important to understand the wants and needs of future users and who will the early adopters will be. They will influence how technologies evolve and if they provide enough benefits to reach the early majority. Innovative Web Monitoring Technologies, User Generated Content (UGC) and the method of Innovation Mining were used within an Autonomous Driving context to understand user’s debate on the Internet. UGC is characterized by extensive volunteering effort, lack of central control and freedom of expression, while creating a basis for identifying and understanding opinions, desires, tastes, needs and decision-making influences of customers in a passive non-intrusive manner. UGC is perceived as being impartial and unbiased, while giving the chance to understand needs and doubts of the potential customers, as well as the used language within a certain topic. The method of Innovation Mining presented below reflects the process from the search for the UGC until the possible visualization and interpretation of the gained information. • Analysis of the users language within an AD context • Most relevant single sources of discussion • Topic evolution including most impactful events • Brand importance in the users perspective • Most mentioned activities in an AD vehicle • In depth language analysis of concepts and their drivers

The Race to 2021: The State of Autonomous Vehicles and a "Who's Who" of Indus...

Altimeter, a Prophet Company

[Updated 2/27/17] Brian Solis, principal analyst of Altimeter, a Prophet Company, has tracked the autonomous industry for two years and has assembled the most comprehensive report on “The State of The Autonomous Driving.” The updated report features the latest developments among companies driving the future, including 76 automakers, startups and universities. The report also includes an infographic that organizes all of the companies by technology focus and its open to third party creative commons use. This report will be updated regularly, if you would like to contribute updates please contact Brian via email at brian@altimetergroup.com

Dc Comics / BatmanvSuperman / Shazam Case Study

Ekrem Şişeci

Implementation of a lane-tracking system for autonomous driving using Kalman ...

Francesco Corucci

Automated & Autonomous Driving

Roger Atkins

1) Cameras will be the dominant sensor for advanced driver assistance systems (ADAS) and automated driving, though radar and lidar may provide redundancy. 2) Vehicle-to-everything (V2X) communication allows vehicles to share information to deliver safety benefits beyond line-of-sight detection. 3) Security and protecting vehicle systems from attacks will be important as automated functions require data exchange between electronic control units and wireless connectivity increases.

Wenyuan xu Minrui yan can you trust autonomous vehicles_slides_liu_final

PacSecJP

This document discusses contactless attacks against sensors used in autonomous vehicles. It describes how ultrasonic sensors, millimeter wave radars, and cameras can be attacked using jamming, spoofing, or blinding techniques. Experiments were able to cause parking assistance systems and Tesla's Autopilot to malfunction or behave unexpectedly by attacking ultrasonic and radar sensors. Cameras could be blinded using infrared LEDs or laser pointers. The researchers conclude that sensors need to be designed with security in mind to prevent intentional attacks, and that fully autonomous vehicles will need redundant sensors and data fusion to achieve full security.

Roadmap to Future Roads

Ali Maleki

Autonomous cars

Anmol Parimoo

Junli Gu at AI Frontiers: Autonomous Driving Revolution

AI Frontiers

NVIDIA CES 2016 Press Conference

NVIDIA

At a press event kicking off CES 2016, we unveiled artificial intelligence technology that will let cars sense the world around them and pilot a safe route forward. Dressed in his trademark black leather jacket, speaking to a crowd of some 400 automakers, media and analysts, NVIDIA CEO Jen-Hsun Huang revealed DRIVE PX 2, an automotive supercomputing platform that processes 24 trillion deep learning operations a second. That’s 10 times the performance of the first-generation DRIVE PX, now being used by more than 50 companies in the automotive world. The new DRIVE PX 2 delivers 8 teraflops of processing power. It has the processing power of 150 MacBook Pros. And it’s the size of a lunchbox in contrast to other autonomous-driving technology being used today, which takes up the entire trunk of a mid-sized sedan. “Self-driving cars will revolutionize society,” Huang said at the beginning of his talk. “And NVIDIA’s vision is to enable them.”

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.

Back to the Future: The Road to Autonomous Driving

Boston Consulting Group

A decades-old dream is on the verge of coming true. Autonomous vehicles (AVs) will hit the road as early as 2017, when several original equipment manufacturers (OEMs) and technology companies plan to launch pilot projects or roll out commercial vehicles with varying levels of self-driving capability. Mass adoption of self-driving technology will deliver tremendous economic benefits. But it will also disrupt business as usual for a wide variety of companies, including OEMs, mobility providers, and component makers. The coming AV era raises urgent questions for executives of these companies: What is the cost of these technologies and what are consumers willing to pay for them? How fast will mass markets adopt AVs and how might car sharing and societal shifts impact these introductions? What technological challenges must be overcome to enable fully autonomous driving? Where should OEMs and new entrants focus their R&D investments? And how should players in the AV market address consumer concerns around safety, lack of control, and the risks of faulty technology?

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.

Presentation on driverless cars by shahin hussan

Shahinhussan

This document discusses driverless car technologies including how cars will detect traffic lights using light sensors, technologies that enable fully autonomous systems like ABS and electronic stability control, and how vehicles will be controlled. It also covers cruise control, night vision, lane departure warning, adaptive high beams, self-parking, rear cameras, and automated guided vehicle systems. The goal of driverless cars is to reduce accidents by taking on driving tasks and allowing occupants to rest or focus on other things.