Connected car News:

In connected car news this week were Tesla, Car2Go, Elektrobit, StradVision, VBOX,

Tesla Buys Hibar

Tesla quietly bought Hibar Systems Ltd. Hibar Systems is a leader in precision-making of small cell batteries through a highly mechanized pump injection system.

Tesla Battery Degradation Recall Request

On September 19, 2019, the Office of Defects Investigation (ODI) received a defect petition dated September 17, 2019, requesting that NHTSA initiate a defect investigation of certain Tesla Model S and Model X vehicles that received revised battery management software in one or more over-the-air (OTA) updates from Tesla, beginning in May 2019. The Petitioner alleges that the software updates were in response to a potential defect that could result in non-crash fires in the affected battery packs and that Tesla should have notified NHTSA of the existence of this potential defect and conducted a safety recall. The petitioner also alleges that this software update reduces the driving range of the affected vehicles. A copy of the petition will be added to the public file for this defect petition and ODI will evaluate the petitioner’s allegations to determine if the petition should be granted or denied.”

car2Go Exits Cities

car2go made the difficult decision to exit the following North American cities: Austin, Calgary, Denver and Portland effective October 31st; and Chicago effective December 31st.

Car2Go is now refocusing our efforts and our resources on the cities that present the clearest path to free-floating carshare success: New York City, Washington D.C., Montreal, Vancouver and Seattle.

Elektrobit  & MBition

Elektrobit (EB), a cutting edge global supplier of embedded and connected software products for the automotive industry, has signed a long-term collaboration agreement with MBition, a wholly owned subsidiary of Daimler AG. This was the ideal start for the opening of a new software lab in the innovative and creative environment of Germany’s capital Berlin.

MBition selected EB as a key partner to design and implement the software platform for the next generation of Mercedes-Benz telematics systems. Drivers will benefit from enhanced and latest state of the art user experience features developed by MBition. “Having EB with its profound experience in Berlin close to our office in Berlin helps us tremendously to develop software for our future Mercedes-Benz cars,” said  Gregor Zetsche, CEO and managing director at MBition.

StradVision Side Vision Cameras

With the goal of improving safety for cyclists and pedestrians, StradVision has announced it is partnering with a leading global Tier 1 supplier on a side-camera project that is being completed in cooperation with five different truck manufacturers.

The project has been launched and will be completed by 2020. No date has been set for trucks including StradVision software to be on the road.

StradVision CEO Junhwan Kim said one benefit of the arrival of autonomous vehicles is the ability to solve longtime problems like the dangers facing cyclists and pedestrians from trucks.

“StradVision’s deep-learning SVNet software can help ensure that many lives will be saved in the years to come, due to trucks being able to more accurately detect the presence of cyclists and pedestrians,” Kim said. “Blindspot truck accidents, which are now common, have the potential to become something rare, and we are proud that StradVision will play a role in saving lives.”

The ADAS functions this project will center on are Blind Spot Detection and Lane Detection. They will allow trucks to better recognize the presence of even the smallest objects around them.

The technology will be based on StradVision’s SVNet software, which is designed to function properly even in poor lighting or weather conditions.

SVNet has three key elements: SVNet External, SVNet Internal and SVNet Tools.

For this project, the SVNet External software will be the key component. SVNet External enables vehicle camera systems to execute ADAS and self-driving functions, including Lane Departure Warning and Lane Keeping Assist.

In terms of Lane Detection, SVNet is trained to recognize all varieties of lane markers — from solid line, dotted line, double line and even bicycle lanes.

StradVision’s deep-learning expertise is a key reason for its growth, as the company’s algorithms lead the industry in Object Detection (OD) and Free Space Detection (FSD).

Among StradVision’s other projects are work on a Level 4 automated bus in Germany; a sedan in Germany; and SUV, sedan and bus projects in China.

Overall, by 2021 StradVision will have nearly 7 million vehicles on the world’s roadways using SVNet, which is compliant with standards such as Euro NCAP and Guobiao (GB) in China. StradVision is already deploying ADAS vehicles on Chinese roads.

VBOX VIPS

The new VBOX Indoor Positioning System (VIPS) from Racelogic is a highly accurate way of measuring speed, position and attitude in areas where GPS is not available, with industry leading accuracy of ±2 cm.

The system is designed to be used for high dynamic vehicle test and validation procedures which are normally performed outside using GPS. By enabling the same test equipment that has traditionally been limited to outdoor use, to now be used anywhere with limited or no satellite coverage, VIPS is suitable for a wide range of applications and can also reliably fill gaps in GPS coverage. Common use cases include braking, handling, crash testing and ADAS sensor validation, with typical environments where you might benefit from using VIPS being tunnels, urban canyons, indoor test facilities and those with heavy tree cover.

VIPS is the result of five years of research and development that was conducted with the help of existing VBOX customers, vehicle manufacturers and proving grounds around the world. The system provides an elegant solution to one of the most difficult problems faced by the Automotive Testing industry and is also the latest in a long line of innovations from Racelogic; enabling customers to test and validate the very latest safety systems and sensors in a fast and efficient manner in any environment.

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