Lifeboat Foundation

Google today is announcing a HD version of its vehicle mapping solution. Unlike Google Maps, Google’s HD map is not a consuming-facing application, but an additional layer of data that’s served to the vehicle’s L2+ or L3 assisted driving systems through Google Automotive Services.

The additional information sits on top of Google Maps’ data and delivers details such as precise lane makers and localization of objects (road signs) to help assisted driving vehicles orient themselves on the road. The driver will not be able to see or access the HD map or data directly. It’s not clear at this time if the driver will even know if the vehicle is using the HD mapping, though, presumably the vehicle’s assisted driving skills will be improved when it’s in use.

According to a Google spokesperson, the HD mapping is initially focused on high-traffic roads like freeways, but the spokesperson stopped short of saying exactly which cities or freeways. They said Google is working with automakers to determine where the HD map is most helpful.

Artificial Intelligence is the buzzword of the year with many big giants in almost every industry trying to explore this cutting-edge technology. Right from self-checkout cash registers to AI-based applications to analyse large data in real-time to advanced security check-ins at the airport, AI is just about everywhere.

Currently, the logistics industry is bloated with a number of challenges related to cost, efficiency, security, bureaucracy, and reliability. So, according to the experts, new age technologies like AI, machine learning, the blockchain, and big data are the only fix for the logistics sector which can improve the supply chain ecosystem right from purchase to internal exchanges like storage, auditing, and delivery.

AI is an underlying technology which can enhance the supplier selection, boost supplier relationship management, and more. When combined with big data analytics AI also helps in analysing the supplier related data such as on-time delivery performance, credit scoring, audits, evaluations etc. This helps in making valuable decisions based on actionable real-time insights.

SEOUL (Reuters) — South Korea’s antitrust regulator said it would impose a 2.85 billion won ($2.2 million) fine on Tesla Inc for failing to tell its customers about the shorter driving range of its electric vehicles (EVs) in low temperatures.

The Korea Fair Trade Commission (KFTC) said that Tesla had exaggerated the “driving ranges of its cars on a single charge, their fuel cost-effectiveness compared to gasoline vehicles as well as the performance of its Superchargers” on its official local website since August 2019 until recently.

The driving range of the U.S. EV manufacturer’s cars plunge in cold weather by up to 50.5% versus how they are advertised online, the KFTC said in a statement on Tuesday.

Using radar commonly deployed to track speeders and fastballs, researchers have developed an automated system that will allow cars to peer around corners and spot oncoming traffic and pedestrians.

The system, easily integrated into today’s vehicles, uses Doppler radar to bounce radio waves off surfaces such as buildings and parked automobiles. The radar signal hits the surface at an angle, so its reflection rebounds off like a cue ball hitting the wall of a pool table. The signal goes on to strike objects hidden around the corner. Some of the radar signal bounces back to detectors mounted on the car, allowing the system to see objects around the corner and tell whether they are moving or stationary.

“This will enable cars to see occluded objects that today’s lidar and camera sensors cannot record, for example, allowing a self-driving vehicle to see around a dangerous intersection” said Felix Heide, an assistant professor of computer science at Princeton University and one of researchers. “The radar sensors are also relatively low-cost, especially compared to lidar sensors, and scale to mass production.”

As lidar company Luminar pushed ahead to meet its goals for 2022 — milestones that included locking in new commercial contracts with unnamed automakers and shipping production-ready sensors to SAIC — it also snapped up a small HD mapping startup called Civil Maps.

The acquisition, which was disclosed Wednesday during Luminar founder and CEO Austin Russell’s presentation at CES 2023, is more than just a large publicly traded company taking advantage of a consolidating industry. Although the timing couldn’t have been better due to the current economic environment, according to Russell.

For Russell, the acquisition is part of Luminar’s longer term vision to be more than just a lidar supplier. Mapping, specifically the mapping tech that Civil Maps created, is foundational to that goal, Russell said.

We can expect to the latest and greatest in gaming and computer hardware, audio visual, electric vehicles and smart home technologies.

250 feet down a cliff. Notice many of the Musk bashing news outlets are not reporting this. #PleaseShare

Montara, Calif. — A 4-year-old girl, a 9-year-old boy and two adults survived Monday after their car plunged off a Northern California cliff along the Pacific Coast Highway near an area known as Devil’s Slide that’s known for fatal wrecks, officials said.

The Tesla sedan plummeted more than 250 feet from the highway and crashed into a rocky outcropping. It appears to have flipped a few times before landing on its wheels, wedged against the cliff just feet from the surf, according to Brian Pottenger, a battalion chief for Coastside Fire Protection District/Cal Fire.

Continue reading “2 adults and 2 children survive after Tesla plunges 250 feet off California cliff: ‘An absolute miracle’” »

Sensing systems are becoming prevalent in many areas of our lives, such as in ambient-assisted health care, autonomous vehicles, and touchless human-computer interaction. However, these systems often lack intelligence: they tend to gather all available information, even if it is not relevant. This can lead not only to privacy infringements but also to wasted time, energy, and computational resources during data processing.

To address this problem, researchers from the French CNRS came up with a concept for intelligent electromagnetic sensing, which uses machine-learning techniques to generate learned illumination patterns so as to pre-select relevant details during the measurement process. A programmable metasurface is configured to generate the learned patterns, performing high-accuracy sensing (e.g., posture recognition) with a remarkably reduced number of measurements.

But measurement processes in realistic applications are inevitably subject to a variety of noise. Noise fundamentally accompanies any measurement. The signal-to–noise ratio can be particularly low in indoor environments where the radiated electromagnetic signals must be kept weak.

Year 2021 face_with_colon_three

In recent years, the use of deep learning in language models has gained much attention. Some research projects claim that they can generate text that can be interpreted as human writing, enabling new possibilities in many application areas. Among the different areas related to language processing, one of the most notable in applying this type of modeling is programming languages. For years, the machine learning community has been research ing this software engineering area, pursuing goals like applying different approaches to auto-complete, generate, fix, or evaluate code programmed by humans. Considering the increasing popularity of the deep learning-enabled language models approach, we found a lack of empirical papers that compare different deep learning architectures to create and use language models based on programming code.