The rolling chassis from Schaeffler, a modular platform for new mobility concepts, is combined with the Mobileye Drive self-driving system. The goal: to develop a new, flexible platform for self-driving shuttles and other vehicle products at full automation level 4 and to offer customers worldwide solutions for Mobility-as-a-Service (MaaS) and Transportation-as-a-Service (TaaS).

Autonomous transport solutions from 2023
By combining Mobileye’s AV technology with Schaeffler’s rolling chassis, both companies can offer an autonomous, highly flexible and adaptable vehicle platform that meets automotive safety standards with the necessary redundancies and thus enables the rapid scaling of autonomous transport solutions from 2023. Mobility service providers and transportation of goods companies will thus pave the way for the introduction of autonomous shuttles economically viable, as operating times and efficiency can be significantly increased.

The Rolling Chassis from Schaeffler is a flexible, scalable platform for new, driverless mobility solutions for the transport of people or goods or for special applications such as mobile charging solutions or pop-up stores on wheels. The modular platform shows the wide range of technologies from Schaeffler and offers a flexible architecture: Regarding steering and drive, a wide variety of variants can be implemented depending on customer requirements – from a simple drivetrain via an e-axis and central steering to the use of four “Schaeffler Corner Modules”. The corner modules, which each allow a steering angle of up to 90 degrees, have been further developed in the direction of series production and scalability. They include the wheel hub motor, the wheel suspension including air suspension, which makes it possible to lower the vehicle for entry, the actuator for the electromechanical steering and a brake.

The Mobileye Drive self-driving system is a turnkey AV solution that delivers safety via two core concepts: Mobileye’s formal Responsibility-Sensitive Safety model for the safety of the system’s decision-making, and a perception system featuring True Redundancy whereby two independent subsystems (cameras and radars+lidars) combine to enable robust perception. The self-driving system can also be deployed without geographical limitation thanks to Mobileye’s Road Experience Management AV mapping technology through which a proprietary, crowdsourced AV map of the global road network is created and then continuously and automatically updated using data gathered from mass-market advanced driver-assistance systems.

You can find all Schaeffler press releases about the IAA Mobility in the digital press kit:
https://www.schaeffler.com/content.schaeffler.com/en/news_media/press_kit/presskitsdetail/press_kit_iaa.jsp

The so-called Data Dam project is one of the cornerstones of the Korean Digital New Deal announced last year to help the country’s digital economy adapt to the post-COVID-19 era.

A water-storage dam collects, stores water, and distributes it to the surrounding land for activities, such as farming. Just like the water dam, the Data Dam collects information from public and private sectors to create useful data and releases the data across all industries. The goal is to allow people from all walks of life, as well as businesses, to easily access and make the most of useful data that meet their needs.

The Data Dam is a project to collect data generated through public and private networks and to standardize, process, and utilize the data to create smarter AI. In the process, innovative services powered by AI will be developed and a host of jobs will be created across the board.

Recently, MSIT, together with National Information Society Agency (NIA), kicked off the project in earnest by releasing 170 types of data for AI learning on an integrated data platform, called AI Hub (https://aihub.or.kr/).

Disseminating AI Data Across Industries

The release has led to a significant increase in the amount of content materials on AI, a key technology affecting the full spectrum of society and industrial sectors, including education, healthcare, transportation, and finance.

The released data come to 480 million pieces collected from eight key business areas: language such as spoken dialects; healthcare such as diagnostic images for cancer and brain disease; self-driving vehicles such as on-road driving videos; visual data that showcase sports motions; environment such as images of water pollution; agriculture and fisheries such as diseases affecting crops, livestock, and fish; social safety such as images of decrepit facilities; other areas including images of fashion goods.

In particular, data collected from “Korean services,” for example, involving language, transportation, and medical records, are expected to help the nation accelerate the development of AI-powered services in the future. The innovative services will, in turn, benefit people and improve the quality of their lives.

Among others, data about Korean-spoken dialects — from Gyeongsang-do, Jeolla-do, Chungcheong-do and Gangwon-do Provinces as well as Jeju Island — will help make up for the shortcomings of existing voiced-based AI services that could not recognize dialects well. According to the results of the data performance evaluation, the new data on spoken dialects sound more natural, with increased recognition rate by 12%.

Data on self-driving vehicles include a variety of visual materials that showcase not only on-road driving, but also parking obstacles and moving object detection. This information will help accelerate the development of future autonomous vehicles.

People Build Data Dam Together

The Data Dam project is the outcome of joint efforts by the whole nation as the government, professionals from public and private sectors and citizens joined forces to build the dam together. They engaged in the entire process of data planning, establishment, verification, and management.

In 2020, the MSIT conducted research and analysis of data demand across the public and private sectors. Based on the results, it engaged experts from business, academic, and research areas in planning and establishing a data platform for AI learning. The MIST also adopted “cloud sourcing” tactics in the process so that many citizens — amounting to as many as 40,000 — could be part of collecting and processing data together.

Experts, organizations, and businesses from the eight target areas participated in the process of verifying and managing the data quality and usability, too. For data quality control, the MSIT set up an advisory committee consisting of more than 80 experts who will regularly evaluate the quality of data available on the AI Hub platform.

In addition, a council of the Telecommunications Technology Association (TTA) and public-private entities was launched with the aim of expanding AI data usability across the board.

The government expects that the Data Dam project will quench the thirst for useful data, which is essential for the development of AI services. The dam will disseminate useful data to diverse businesses, especially small and medium-sized companies and startups with insufficient budget and human resources.

U-M’s Ford Motor Company Robotics Building is a four-story, $75 million, 134,000-square-foot complex situated on the north campus. As the new hub of the U-M Robotics Institute, its first three floors hold custom U-M research labs for robots that fly, walk, roll and augment the human body – as well as classrooms, offices and makerspaces. Through a unique agreement, the fourth floor houses Ford’s first robotics and mobility research lab on a university campus, as well as 100 Ford researchers and engineers.

The new facility brings together U-M researchers from 23 different buildings and ten top-10 programs. With the new infrastructure, researchers working on two-legged disaster response robots can test them on a 30-mph treadmill studded with obstacles, or on a stair-stepped “robot playground” designed with the help of artificial intelligence, for example. Biomedical engineers will have access to “earthquake platforms” with force-feedback plates to guide their development of lighter-weight, more efficient prosthetic legs. And Ford engineers will explore how their upright Digit robots can work in human spaces while taking AVs from robotic computer simulations to on-road testing at U-M’s world-class proving ground just down the road.

For Ford, the facility is key to the company’s transformed and modernized research and product development processes aimed at disrupting the transportation landscape. Key research will focus on:

• The future of moving goods more efficiently, as the pandemic further fuels online retail growth. Ford recently purchased several Digit robots, the first commercially available robot with arms and legs, to work with humans and in human spaces.
• Experimenting with a four-legged robot, Spot, to laser-scan plants – helping engineers update the original computer-aided design used to prepare to retool for new products.
• Self-driving vehicles and their role in the future of cities. Members of Ford’s autonomous vehicle team will work at the new facility. Ford’s approach incorporates purpose-built vehicles into a comprehensive and integrated autonomous vehicle operating system that enables choice, convenience and value for the movement of both people and goods.

In a blog post, Aurora announced a long-term, global, and strategic collaboration with Toyota and Denso, one of the largest global manufacturers and tier-one automotive suppliers, respectively, to build and globally deploy self-driving cars at scale.

This collaboration has two primary goals:

• It brings the engineering teams together to develop and test driverless-capable vehicles equipped with the Aurora Driver, starting with the Toyota Sienna. By the end of 2021, the companies expect to have designed, built, and begun testing an initial fleet of these Siennas near our areas of development, including the Aurora Test Site Network.
  
• It brings the two companies together to lay the groundwork for the mass-production, launch, and support of these vehicles with Toyota on ride-hailing networks, including Uber’s, over the next few years. As part of this long-term effort, we’ll be exploring mass production of key autonomous driving components with Denso and a comprehensive services solution with Toyota for when these vehicles are deployed at scale, including financing, insurance, maintenance, and more.

The Veoneer system will be the first in the industry to include multiple thermal sensing cameras that provide both narrow and wide field-of-view capabilities to enhance the safety of self-driving vehicles. Thermal sensing cameras excel in driving situations where other sensor technologies are challenged, including low-visibility and high-contrast conditions such as: nighttime, shadows, dusk, or sunrise; while facing direct sun or headlight glare; and in challenging weather conditions including fog and environmental conditions such as smoke. When combined with analytics, thermal sensing cameras can help detect and classify a broad range of common roadway objects and are especially adept at detecting people and other living things, what AVs want to avoid most.

FLIR thermal sensing cores are also part of Veoneer’s fourth generation thermal sensing system scheduled to launch next year. With more than a decade of experience in the automotive industry, FLIR has been a key partner of Veoneer to support driver early-warning systems in vehicles from General Motors, Volkswagen, Audi, Peugeot, BMW, and Mercedes-Benz. This latest contract represents a new phase for FLIR and its thermal sensing technology for the benefit of autonomous, or self-driving vehicles

Electrify America and Stable recognize the emergence of both electric and autonomous vehicle fleets, and are jointly working to address the absence of dedicated charging facilities equipped for such vehicles with this demonstration. The two companies will deploy a pilot solution in San Francisco, CA, aimed at charging autonomous EVs without human intervention using a robotic solution attached to a 150kW DC fast charger. This charging location will be Stable’s first commercial autonomous charging site and is anticipated to be open in early 2020.   

As part of this agreement, Electrify America will evaluate the hardware, network, operations and billing of its charging systems to best suit autonomous charging fleets. Electrify America has provided two 150kW DC fast chargers to Stable’s charging facility for initial development work behind demonstrating the commercial viability of autonomous charging services for self-driving EVs.

Stable will manage the overall project and pair its robotic technology and advanced scheduling software located at its facility with Electrify America’s chargers. The dedicated fleet-charging facility will allow self-driving EV fleets to charge with no operators present: vehicles can park themselves anywhere inside a standard parking space, and Stable’s robot will automate the connection between the vehicle and the charger.

Stable has selected Black & Veatch to be the engineering company for this project. Stable pointed to Black & Veatch’s experience in delivering global infrastructure solutions for cleantech transportation markets as a leading factor for its selection for the role.

As a part of Electrify America’s Cycle 2 plans, the company announced plans to conduct autonomous charging demonstrations to better understand the optimal charging site design, hardware and back-end solutions. Information from this first site will also help both companies evaluate appropriate business models for different autonomous fleet holders across various locations at commercial scale.

While Quantum Signal has operated in relative obscurity over the past couple of decades, its work has been on our radar at Ford for a while now. Over the past few years, Ford has been assembling a team of uniquely qualified experts in software development, simulation and machine learning from all around the world as it accelerates autonomous vehicle development — and we’re proud to announce the squad is getting even bigger and more formidable.

To further advance its goal of launching a self-driving vehicle business, Ford has acquired Quantum Signal and will make use of its extensive experience in real-time simulation and algorithm development, as well as robotics, sensing and perception technology. The wide range of the group’s work portfolio means it will be able to support Ford in numerous areas as the company develops self-driving vehicles and transportation as a service business, including software development and hardware prototyping.

Boxbot’s fleet includes two types of vehicles: parcel delivery vans and self-driving electric vehicles that can manage more complex and expensive deliveries, like those requiring signatures. Boxbot’s vehicles can be loaded with packages throughout the day at its automated local hubs. Automating this process enables Boxbot’s drivers to get more done in less time, without worrying about loading, organizing or finding packages within their vehicles.

The Aurora Driver is Aurora’s self-driving platform that comprises the hardware, software, and data services that guide vehicles powered by it safely through the world. Delivering level 4 autonomy, Aurora Driver is composed of technology that is able to sense and navigate the environment without human intervention.

Through this proposed partnership, FCA and Aurora would integrate the Aurora Driver into FCA commercial vehicle lines. This would allow for a variety of customized solutions for commercial vehicle customers at a time when changing lifestyles and online shopping patterns are creating logistical opportunities.

Designated as Korea’s first free economic zone, IFEZ consists of Songdo, Yeongjong and Cheongna International Cities with a total size of 132.9 km².

SK Telecom will create an HD map covering the entire IFEZ area starting with Songdo International City. An HD map is a machine-readable map with centimeter-level accuracy, offering essential information for self-driving, including lanes, road conditions, speed limits and road slopes. Thus, it is regarded as a key element for achieving Level 4 of autonomy for autonomous vehicles as defined by the Society of Automotive Engineers.

The HD map will be built with ‘Dynamic Data Platform,’ a 5G-based real-time update solution for HD maps. Dynamic Data Platform automatically updates HD maps upon receiving road observation data from Advanced Driver Assistance Systems (ADAS) over 5G network. SK Telecom plans to install ADAS and 5G communication modules to public transport vehicles and official/government cars in the IFEZ area to detect all changes in the road environment.

Moreover, SK Telecom and the IFEZ Authority will build a data hub to systematically manage and analyze data of the floating population to enhance the convenience and safety of citizens through smarter transportation policies.

Under the MOU, both parties will launch a suite of solutions which includes Argus’ in-vehicle solutions and technologies, and Argus’ stand-alone cyber security backend platform. This platform collects, correlates and analyses data derived from vehicles, mobile apps, cellular networks, cloud platforms, and other sources, and will be integrated with the managed security services portfolio of Trustwave, Singtel’s cyber security arm. The platform helps automakers and fleet managers to quickly protect vehicles that are already on the road without making any modifications to them. It will be delivered through Trustwave’s global network of 10 Advanced Security Operations Centres, further enhancing its capabilities as a leading global managed security services provider.

As part of the collaboration, Singtel and Argus will conduct cyber security research and development at the Singtel Cyber Security Institute (CSI) to help defend autonomous vehicles against sophisticated, evolving cyber threats. The partners could also use CSI’s advanced cyber range platform to subject their solutions to rigorous testing and prototyping. This augments Singtel’s ongoing collaboration with the National University of Singapore to develop advanced cyber security solutions for next-generation cyber threats.

The announcement of the partnership is timely as the introduction of autonomous vehicles in Singapore is one of the initiatives which the government has set out as part of the Smart Nation plan.

FABU has deployed its autonomous driving technologies on the road. Beginning in 2017, five light trucks outfitted with a FABU L4 autonomous driving solution have self-driven a daily 160-mile round trip route on the streets and highways of Zhejiang province in China. The trucks have completed nearly 50,000km/31,000 miles of safety tests in all weather conditions, achieving a camera and sensor perception range of 200 meters/650 feet and the obstacle detection accuracy of 97 percent. In the next phase of road testing, FABU plans to implement their AD technologies in vans and heavy trucks.

To meet the specialized data processing requirements of ADAS and AD, FABU has developed a new architecture made-to-order for deep learning and AD algorithms, called the Massive, Parallel and Volumetric (MPV) architecture. The MPV design facilitates the creation of large-scale computing modules that provide programmable and resilient architecture support for key AD and ADAS capabilities such as sensor fusion, object identification, and tracking, event forecasting and planning. Chips based on the MPV architecture are also optimized for high-performance efficiency and low power consumption.

FABU’s Phoenix series of automotive safety integrity level AI chips encompass the full algorithmic requirements of autonomous driving by addressing three stages of AD data processing: sensor input and perception, sensor data integration and fusion, and smart automated decision making. The key component for the Phoenix-100, the deep learning accelerator, conducts real-time, high precision 3D perception of the environment and was successfully taped out with silicon functionality verified in late 2018. FABU plans to launch its automotive-grade sensor fusion chip Phoenix-200 in 2019 and its Phoenix-300 automated decision-making chip in 2020. By developing and integrating this series of AI chips, FABU will establish a driverless computing platform named The Phoenix, which will extend autonomous driving capabilities from L3 to L5.

To prepare for CES, the team acquired a car locally and retrofitted it with the necessary sensors and hardware for self-driving operation. The team then used Yandex’s mapping technologies to build a custom HD map of the neighborhood around the hotel. Yandex’s proprietary self-driving software stack was then localized and optimized to the driving conditions of Las Vegas. The transformation of both the car and the software was completed in only one month. The route tests the Yandex self-driving car against unprotected left-hand turns, pedestrians, and busy traffic with speeds up to 45 mph.

Las Vegas was the first test location outside of Russia for Yandex’s self-driving car unit, and in addition to the CES demo, Yandex recently announced it is expanding its self-driving tests to Israel.

Working with academics and experts from leading businesses in the car industry including Jaguar Land Rover, Ford and Bentley, as well as the National Cyber Security Centre, and funded by the Department for Transport, the British Standards Institute developed the guidance to set a marker for those developing self-driving car technology.

Perceptive Automata’s core technology takes sensor data from vehicles that show interactions with people. This rich data is used to train deep learning models to interpret human behavior the way people do. The end result is sophisticated AI software that can be integrated into autonomous driving systems. With the software installed, autonomous vehicles can anticipate what pedestrians, cyclists and motorists might do next.

PonyAlpha features improvements across hardware and software from Pony.ai’s earlier generation prototype vehicles. In terms of hardware, the new system includes increased sensor coverage provided by additional LiDARs, radars, and cameras. In order to support these additional sensors, PonyAlpha also features a more highly optimized hardware platform, resulting in a tightly integrated full-stack system. This sensor suite and configuration is a product of extensive testing across varied US and China environments. Altogether, the full sensor suite can see distances of roughly 200 meters, enabling a wider variety of driving scenarios at even greater speeds.

The change in PonyAlpha’s algorithms comes from its deep and adaptive sensor fusion module. Due to the multi-sensor approach (LiDAR, radar, camera). Pony.ai’s sensor fusion technology is able to intelligently use the most reliable sensor data depending on different environmental or driving scenarios. In addition to rolling out PonyAlpha, the Pony.ai team has been working on several other critical fleet capabilities: the Pony.ai Vehicle Control Center and its in-vehicle interface PonyHI (Pony Human Interface). The Pony.ai Vehicle Control Center enables centralized management, tracking, and dispatching of all vehicles in its fleet.