A quiet ride

Bose is developing a new noise-canceling system for your car. The audio system will use microphones and algorithms to cancel out sound.

Conventional methods to reduce noise would include extra insulation and specialized tires from driving on uneven pavement and rough roads. Bose is using another method with accelerometers mounted on the vehicle’s body and microphones inside the vehicle to measure vibrations that create noise. An advanced software can then process the signals and use the vehicle’s built-in audio system to electronically control unwanted sound by sending out an acoustic cancellation signal through the vehicle’s speaker.

The production is planned to start by the end of 2021 and Bose will collaborate with automakers during the vehicle development process to install its tech during production.

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5 trends reshaping the cars

Are you tired of driving your own car? Want to know when self-driving vehicles will actually be available?

A few trends are reshaping the way we will travel. We have listed 5 trends that will have an impact on how we use our cars in the future.

  1. Electrical cars
  2. Connectivity and IoT
  3. Self-driving cars
  4. Services instead of owning – Pay-per-use
  5. New HMI

Connected and self-driving cars can reduce more than 90 percent of accidents caused by human error. They could also help to eliminate hours wasted in traffic and reduce congestion. The automakers all race to put these cars on the road but however, the industry is facing regulations that limit the adoption of this new technology. The industry is therefore divided on when we will have the first commercial self-driving car. Some is saying 2020, while others expect it to take more than 10 years.

One important technology to make self-driving cars is the LiDAR (light detection and ranging) sensors. It can be found in vehicles that offer ADAS (advanced driver assistance systems) features such as pedestrian detection with automatic emergency braking. LiDAR is a complement to the radars and cameras.

Self-driving cars need to combine all three different sensors and form the right mix to solve all issues to drive safe. Increased production of self-driving cars will help to decrease prices of LiDAR but it could take a few years. Partnerships among automakers, suppliers and sensor makers will play a role in LiDAR proliferation and price declines, too.

For any type of driverless vehicle, the first thing you need to do is to create a perception module. This enables the vehicle to react to shapes and textures around it, including people, vehicles, obstructions, lane markings and traffic light colors. While cameras can detect both types of information, radar and LiDAR recognize only shapes.

Cameras are good to use as the primary source of information for perception, and then use LiDAR and radar as a redundancy for shape. All sensors combined, it is possible to build a very accurate model of the environment around the vehicle.

When automakers prepare for a world of self-driving cars, they’re experimenting with many different types of human-machine interface technologies (HMIs) including interior-facing cameras, gesture and voice controls, and touch-sensitive surfaces to integrate with the driver. This is all supported by new and smarter computing platforms.

In the race for the next generation battery, lithium-ion technology has improved a lot during the years. But same problem as in the oil industry, the power packs use raw material mined in unstable countries, and they’re dangerous if they break. Many companies are spending money to find new technologies making it possible to store more energy so the power could last longer at a lower cost.

We still don’t know how electrical and autonomous vehicles will be sold or marketed in the future. Automakers have hinted that ride-sharing and shuttle services may become their primary market and it will be interesting to see how the pay-per-use services will be developed.

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Big data and mobility

Predictive analytics will save lives

Big data used to be a term that refers to a large volume of data. Over the years the term has expanded to include the ability to capture, store, and manage data to use analytics to make predictions about future events.

The integration of digital technologies and use of data in our daily lives occurs at an exponential pace. Companies in the automotive industry are finding new and efficient ways to analyze data to increase traffic safety by predicting behavior.

Car crashes killed more than 35,000 people in the U.S. 2018. Most of them because of human errors. They were attributed to more than 90% of the crashes. The autonomous technology represents a great opportunity to improve traffic safety.


In 2019, we will see new efforts to combine technologies with products, using IoT to stream data and apply Machine Learning in real time.

Instead of using stored data from a traditional controlled environment it will be more common to process live streamed data with help of new frameworks. Daily intervals of data are often not sufficient. Predictive analytics is expected immediately.

With help of multiple sensors, including lidar, radar and cameras, automotive companies can process and analyze new data in real-time. Stream processing help users query continuous data streams and detect conditions immediately after receiving the data. Speed is necessary and new data has to be processed as soon as it’s known to the system.

Faster processing time will help to predict traffic behavior in real-time and enable automotive companies to emphasize on proactive safety rather than reactive safety. This paradigm shift will prevent a majority of car accidents, especially those who are attributed to human errors.

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