Russian project to develop fleet of self-driving delivery trucks is underway

7 March 2019

The Russian mining industry management system developer VIST Group, a subsidiary of industrial digital technology corporation Zyfra, has teamed-up with Russian truck and engine manufacturer KAMAZ and Nazarbayev University in Astana, Kazakhstan to launch an autonomous truck initiative. The hope is to develop an autonomous KAMAZ truck fleet for long-distance transportation.

The new project builds on VIST's success in a collaboration with Belarusian earthmoving equipment manufacturer BelAZ. In late 2018, at a testing ground in Belarus, VIST and BelAZ demonstrated a remote-operated 130-ton BelAZ-751R dump truck and a fully-automated BelAZ-78250 front loader as they worked together to move mounds of dirt. The project was part of VIST's "Intelligent Mine" project, or the creation of fully-automated open mines that will lessen the risks of human injury while allowing for 24-hour production.

For VIST's new collaboration a KAMAZ 5490 Neo chassis, designed as a flagship for the KAMAZ fleet, will be fitted with six Orlaco EMOS low latency ethernet cameras, with aperture angles of 60° or 120°. The EMOS camera is ruggedized for use with onboard computer systems in trucks and heavy machinery, has a latency below 100 ms, and can stream MJPEG or H.264 via RTP and AVB protocols. The KAMAZ 5490 will also be equipped with short and long-range radars manufactured by Continental Automotive, and VLP-16 "Puck" LiDAR manufactured by Velodyne. An NVIDIA Jetson TX-2 module will process and synchronize the data received by the various sensors.

"Computer vision can be used to solve tasks such as lane following, traffic light and sign recognition, and object detection and classification, which are not possible with conventional sensors, including radars and lidars," said Artyom Fedotov, head of the KAMAZ project at VIST Group. "The current state of the technology does not allow precisely and robustly identify the distance, spatial measures or velocity of the surrounding objects, however. In this project we plan to blend all possible sensor inputs like cameras, radar, and lidar to build a robust autonomous truck where each and every sensor can complement or substitute for other sensors."

“We are managing the engineering operations for the adaptation and improvement of computer vision modules for the unmanned control of the Kamaz Neo vehicle," said project manager Zhandos Yesenbayev, senior researcher at Nazarbayev University. "Together with the VIST Group specialists, who provide us with the necessary additional equipment and software, we have started the project, specifically the operations to introduce vehicle trajectory planning functions into software and generate vehicle control commands to detect and recognize obstacles."

The project is expected to be finalized by September 2019.