The Building Blocks of Autonomous Tech

The Building Blocks of Autonomous Tech

Mimicking the many things humans do while driving requires a complex blend of technologies. An array of several sensors per vehicle is needed to monitor a 360° field of view around the car. Fast networks that send data to the electronic controls are required to analyze inputs and make decisions about steering, braking and speed. OEMs, Tier 1s and other suppliers are vying with and in some cases partnering with and acquiring a relentless wave of start-ups and industry disruptors including Apple and Google, as they race to develop tomorrow’s mobility solutions. Their keys to winning reside in the following technology areas. Processing power The processors that analyze sensor data and make steering, braking and speed decisions will undergo major changes. Today’s safety systems use a range of conventional multicore processors from companies like NXP, Infineon, Renesas, STMicroelectronics and Intel. But the extreme challenges associated with autonomy will require a range of processing technologies. Nvidia’s highly parallel graphic processing units, each with thousands of small processing cores, burst onto the automotive scene in recent years. GPUs excel at processing multiple tasks simultaneously, like analyzing the many pixels streaming in from sensors. Nvidia’s latest multi-chip platform for SAE Level 3 through 5 driving, code-named Pegasus, is the size of a car license plate and delivers datacenter-class processing power—up to 320 trillion operations per second. Mobileye, now owned by Intel, has also developed a dedicated image processor. Specialized parallel devices can be made using field programmable gate arrays (FPGAs) from Xilinx, Intel (nee Altera), Lattice Semiconductor and Microsemi. FPGAs let designers create chips that are optimized for a given task. Mainstream suppliers like NXP and Renesas have licensed programmable technology from Tensilica that is being expanded from infotainment to safety systems. Conventional CPUs won’t disappear. They are best at sequential processing techniques used to make decisions. They’ll help fuse sensor data after it’s processed by various dedicated processors housed on sensors or in the ECU. Most systems today link a processor to each system—for example, lane departure or adaptive cruise control. It’s likely that a large control unit will collect all relevant data and decide how to navigate. That will push the demands for more cores, faster clock rates and a low power budget, especially in EVs.

Read more : https://assets.techbriefs.com/EML/2018/special_reports/12_adas_connected_car/ADAS_Connected_Car_1218.pdf