The uniqueness of the system lies in the fact that the AI analyzes the vehicles’ performance vis-a-vis similar vehicles, unlike the telemetry systems currently available.
By JERUSALEM POST STAFFA SELF-DRIVING car by Google is displayed at the Viva Technology event in Paris, France, in June.(photo credit: REUTERS)
Galooli, an Israeli company which offers advanced remote mobile asset management solutions, has launched an artificial intelligence system for controlling vehicle fleets.Called GalooliOTO AI, the new generation of telemetry systems for autonomous vehicles uses data analysis of more than 40 million data points per day to control the vehicles.The company is currently finalizing its first transaction, which includes supplying the system to more than 100,000 vehicles. The system also enables review and inspection of abnormal driver behavior, the use of advanced safety systems, precise location, mechanical status, working hours, engine loads, pollutant emissions and more.The first models of vehicles with GalooliOTO AI’s advanced system are expected to arrive in Israel in 2019.The group already has more than 4,000 customers worldwide and operates in about 30 different countries through 40 distributors.The uniqueness of the new system lies in the fact that the artificial intelligence analyzes the vehicles’ performance vis-a-vis the organization and similar vehicles, unlike the telemetry systems currently available that view the vehicle as a single stand-alone entity without relevant benchmarking.“Operating and maintenance costs in the autonomous vehicle world will be one of the biggest challenges for rapid penetration and acceptance of the autonomous vehicle as the future means of transport,” said Ronen Barel, Galooli’s CEO. “In the future, it can be assumed that people will not own a vehicle, but they will book one for each trip. It follows that the world of spending and ROI will change completely. Any organization that will manage autonomous vehicle fleets and will want to succeed will have to work according to clear economic models, which include, inter alia, economical driving time and range distribution, preventive maintenance, energy consumption, meeting targets, etc.”
