Like all others, the COVID-19 pandemic affected the autonomous vehicles (AV) industry just as much. China, forecasted to be the world’s largest AV market, saw a dip in its AV sales by 71 per cent at the beginning of the pandemic in February 2020. In other major markets, the situation was similar. Europe saw an 80 per cent fall and the US by 47 per cent. However, production rolled back to its earlier numbers, sometimes even surpassing the production levels from before the first quarter of 2020. 

At present, most of the AVs available in the market are in Level 2 and 3, meaning they have systems including the likes of lane departure warning, collision detection, and cruise control present in these cars. However, it is not too long that AVs built to the Level 4 and 5 make it to the markets. In fact, according to a market research report by Facts and Factors, the AV cars industry was valued at $23.33 billion last year and is expected to grow at a CAGR of 22.7 per cent to reach $64.88 billion by 2026. 

Today, Analytics India Magazine has curated a roundup of all the top headline makers from the AV industry. 

Toyota acquired the AV division of Lyft

Toyota Motor Corp acquired the AV division of US-based company Lyft for $550 million. The former paid Lyft $200 million upfront, and the remaining $350 million will be paid over the next five years. Additionally, Toyota also signed a commercial agreement to use the system and fleet data of Lyft. Lyft started operations of its AV unit in 2017, launching the Level 5 division.  

Bullish about the growing AV industry, Toyota has earlier invested in the US and China-based self-driving startup Pony.ai. It has also developed a driver-assistant system Guardian and self-driving software Chauffeur. 

Ati Motors raises $3.5M

Electric AV-maker Ati Motors raised $3.5 million in its pre-Series A round of investment from Blume Ventures and Exfinity Venture Partners. The Bengaluru-based startup was founded in 2017 by V Vinay, Saurabh Chandra and Saad Nasser. The startup planned to utilise the freshly raised funds to manufacture and deploy a large fleet of AVs in warehouses and factories in both India and abroad.

MIT researchers develop DNN for AVs

MIT researchers have developed a single deep neural network (DNN) for AVs, using NVIDIA DRIVE AGX Pegasus. The paper mentions how the researchers are using a new self-driving technique using the single DNN by processing real-time lidar sensor data. The researchers have also created new enhancements to increase speed and energy efficiencies. The DNN is built with the aim of executing all the operations of the self-driving system. Still in its nascent stage, the approach has the potential to generate considerable benefits. 

Ohio State Center Researchers develop AV Cybersecurity platform 

Ohio State CAR, or Center for Automotive Research, announced that they were developing a dedicated platform for cybersecurity testing for self-driving vehicles or Mobility Cyber Range (MCR). The initial focus of the research pilot was said to be on developing standards and recommendations for AV safety and cybersecurity’s best practices. The technology will be used in connected and autonomous vehicles applications. CAR was also training its students on AI computing by undertaking the development work on NVIDIA. 

Arm’s new open-source architecture and tools

Semiconductor and software design company Arm unveiled its hardware tools for automakers and chipmakers. Its new software architecture and reference implementation– SOAFEE, was released along with two reference hardware solutions. SOAFEE is designed to bring in the real-time safety needs of automotive along with the pros of a cloud-native approach. 

AWS announced AWS IoT FleetWise

At the Amazon Web Services (AWS) re:Invent event, AWS announced the AWS IoT FleetWise, a cost-effective and easier service for automakers to collect, transform and transfer vehicle data into the cloud in real-time. With the help of this service, automakers can collect and organise data in any format available in their vehicles, standardising them for data analysis in the cloud. The service helps transfer real-time parameters like weather conditions, vehicle type and location. Additionally, it can be used to diagnose issues, analyse vehicle fleet health and help reduce potential safety issues.