Perrone Robotics (PRI) attended the TU Automotive Detroit show June 7th & 8th as a track day sponsor offering the only live fully autonomous vehicle demonstration. More than 250 attendees were given rides in the Perrone Robotics vehicle “Linc” where they observed a range of Linc’s features via the UI. PRI’s MAX platform fuses data from three 3D LiDAR units, 6 RADAR units, a stereo vision camera, a camera for lane detection, and an inertial navigation system. Linc demonstrated maneuvers such as self-navigation, vehicle avoidance, pedestrian avoidance, vehicle passing, vehicle following, and a special follow-me mode whereby the vehicle follows a pedestrian around the enclosed facility like a dog on a leash. The vehicle’s built in speech capability kept attendees aware of the vehicle’s every move.
In the trunk was a stand where we could switch out the main processor to show the various platforms we supported to operate the vehicle. We primarily ran the MAX-Auto software stack atop of the Wind River VxWorks operating system demonstrating real-time production-grade automotive controls. The crew also demonstrated MAX’s efficiency and flexibility by running the same profile on a $30 Raspberry Pi 3 and the new Xilinx MP SoC chip running on an Iveia board.
The MAX-Auto software platform is the first and only patented full-stack software platform for production-grade and real-time autonomous drive applications. Customers have the flexibility of swapping in and out different sensors, underlying hardware platforms, and rapidly adding behaviors to the autonomous software stack. With over 14 years of R&D and testing, the MAX-Auto software platform has been honed to be super efficient as well. The ability to run the MAX-Auto platform on a Raspberry Pi demonstrates just how efficient and capable the platform is in scaling up or down. The tuning of MAX-Auto for an industrial grade Intel hardware architecture and ruggedized real-time VxWorks operating environment demonstrates the platform’s capability in running inside of production grade automotive environments.