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Autonomous Vehicle

Microsoft to Help Automakers Develop Autonomous Vehicles

Microsoft is making a push to sell software to automakers looking to develop autonomous vehicles. The company hopes to assist these organizations by offering its Azure cloud, AI edge, and Internet of Things services.

It has partnered with Wayve, an AV startup that has developed machine learning technology that can ‘generalize’ from predefined routes into new cities and areas not previously considered by the system. This capability has attracted two of the UK’s largest grocery retailers – Asda and Ocado Group – to test Wayve’s AV technology as part of their last mile delivery operations in London.

Microsoft’s focus on passenger entertainment

Microsoft’s strategy is to offer cloud computing services, AI and edge computing capabilities, as well as other technologies essential for autonomous driving. As a result, the company is becoming a key player in the automotive industry.

Microsoft also plans to assist a wide range of startups developing self-driving technologies through its accelerator program and partnership agreements. While the companies involved have different focus areas and research directions, all are looking to leverage Azure’s cloud computing and artificial intelligence resources.

Microsoft will prioritize passenger entertainment in autonomous vehicles. To this end, Microsoft plans to provide passengers with a wide variety of content such as audio and video to customize their experience and save them money on radio time.

Microsoft will prioritize safety in autonomous vehicles as another area of focus. In addition to using Azure’s cloud computing capabilities, the company will make sure to monitor and control the vehicle’s safety system.

Safety systems must be able to detect and prevent collisions as well as mitigate traffic congestion’s effects. Furthermore, these systems must have the capacity to act quickly in response to road conditions or other hazards.

Additionally, the company must guarantee that its vehicle safety systems are scalable and can handle vast amounts of data. To do this, they will need to invest in powerful new infrastructures capable of training and testing models with trillions of parameters as well as exabyte-scale image data.

This technology is essential for driverless cars’ future, and much research is being done in this area. Wayve, for instance, has implemented a machine learning approach to driverless cars that enables them to learn about roads they’re driving on. Furthermore, their ‘end-to-end’ driverless car technology enables them to go beyond prescribed routes.

Aside from safety concerns, autonomous vehicles’ capacity to reduce traffic could have a positive effect on the environment. Cities like Singapore have already begun pilot studies with AVs which have significantly reduced vehicle numbers on their streets.

Discovere how self driving algorithms work, click here.

Microsoft’s focus on data

Automakers must invest in a robust data management infrastructure that can process large volumes of information. This includes sensor fusion, simulation, deep learning, training and validation services.

Microsoft is helping automakers address this challenge by offering a suite of services that can ingest, manage and analyze petabytes of data securely, efficiently and cost-effectively. It also provides a platform for creating virtual simulations of cities and roads – helping companies identify and remove roadblocks before they even begin testing in the real world.

An excellent example is Microsoft’s partnership with London-based Wayve, a company that uses machine learning to develop software for autonomous vehicles. It is built upon a global team of experts in computer vision, robotics and artificial intelligence who have tested their driverless cars across different UK cities.

These tests are essential for creating an autonomous vehicle that’s safe and able to navigate a variety of roads without needing expensive Lidar sensors or HD maps. These test cars must learn how to drive in each new city, so their generalization skills must also be tested quickly.

Automakers face a formidable task in collecting millions of miles of data – an endeavor which can be costly. Microsoft, however, offers its customers a comprehensive solution that addresses these obstacles and allows them to move faster with their systems.

Another reason why automakers must have an effective data management infrastructure is to guarantee compliance with regulations. This is particularly critical for firms testing autonomous vehicles on public roads.

Automobile companies must have a system capable of managing massive amounts of data, as this helps them avoid fines and penalties. Furthermore, automakers need to guarantee their drivers are not distracted by other drivers, pedestrians, or traffic lights which could lead to accidents.

Microsoft’s focus on simulation

One of the greatest obstacles in developing self-driving cars is simulating the vast amounts of data generated by these machines. Merging raw sensor data into an integrated model requires massive computing power to accomplish, making this task seem impossible.

Simulation is the key, and Microsoft is taking advantage of its vast Cloud capabilities to assist automotive manufacturers with this process. Through Azure Core and Services, Microsoft will offer global cloud services that let automakers virtualize their infrastructures and networks for ADAS feature development and validation at cost-effective, scalable rates that can be replicated repeatedly.

Automakers will now have the freedom to focus on improving the performance of their entire autonomous vehicle, testing and refining algorithms at a faster rate and lower costs than they otherwise could afford.

Simulating complex tasks requires not only powerful hardware but also extensive software – which Microsoft is providing with its deep AI expertise.

Project AirSim from Microsoft will provide aerospace companies and research universities with an open-source tool that enables them to develop and train artificial intelligence (AI) models in a simulated 3D environment similar to the real world. This will help them create more accurate models for detecting obstacles, avoiding them, as well as performing precision landings.

Project AirSim provides developers with pretrained AI building blocks that have already been optimized for specific use cases. These can then be utilized when testing and honing AI-powered aircraft.

Malhi explained that this will reduce the number of individuals required for developing and testing AI-powered aircraft, making the process more affordable for everyone involved.

Microsoft and dSPACE are joining forces to deliver this goal, offering tools and solutions for data-driven development, simulation and validation. These are tailored to assist OEMs and Tier-1s with the complex development of ADAS applications such as driver assistance systems.

Microsoft’s focus on validation

One of the most difficult tasks for automotive engineers is validating perception algorithms and ADAS systems. These programs are responsible for recognizing objects, navigating, and avoiding obstacles; they require high precision and plenty of computing power to test in real-time.

Thankfully, there are tools that make automating this task easier. For instance, EB Assist Test Lab is an Azure-based platform designed to centralize, store and search driving data while also enabling developers to add new data formats and analysis functions.

Another essential step in validation is creating digital testing scenarios, which can be used to evaluate automated driving system performance under various conditions and environmental elements. This approach may be more efficient and cost-effective than testing in a physical environment which may take more time.

Virtual validation requires the transformation of a large amount of data into various simulated situations. This can be accomplished by creating and importing open-standards simulation scenarios that cover operational design domain (ODD) parameters like weather conditions, traffic signs, road geometry. Once this data has been tested in combination with other vehicle models and third-party simulation tools to confirm the performance of an AD system.

The virtual test scenarios created can be used to verify the performance of an AD system in all possible conditions by incorporating NHTSA framework, New Car Assessment Program (NCAP) and International Organization for Standardization (ISO) standards. Doing this increases accuracy and efficiency when validating AD systems in real-time.

With the rise of automation and self-driving cars, manufacturers are faced with new challenges when it comes to maintaining safety. To meet this goal, they’re employing technologies such as perception algorithms and AI techniques which aim to prevent accidents by sensing when a vehicle may be in trouble and responding accordingly.

Wayve has recently joined Microsoft to leverage their cloud services for autonomous vehicle training and validation. Through the partnership, Wayve will gain access to Azure’s super computers to train and test out its AI models – giving it access to a wider audience while generalizing driving intelligence across new cities – an essential step in the future of AVs.

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