T ransportation
The demonstration used VueForge components and an open-and-con- nected car platform consisting of an enhanced infotainment unit with an Intel®
processor.
The base platform – connected to the car’s CAN bus – provided two levels of information. First, it gave drivers access to a vehicle-spe- cific application through cloud-enabled smartphone services. The smartphone application displayed vehicle status and alerts, advisory information, and key performance indicators such as real-time fuel consumption and estimated total cost of ownership (TCO) against other drivers of the same make and model (Figure 2). The solution based these TCO estimates on driver history and vehicle repair data and prices for standard service actions at approved dealers. Fuel pricing estimates were a statistical estimate.
The platform’s second level of information – delivered to automakers and fleet operators – illustrated the value of statistical data on an entire population of their vehicles (Figure 3). A massively parallel cloud-based simulation generated a 500,000-vehicle data set to demonstrate scaling data collection and analysis. Using such a tool, automakers could track component health and faults, driving environ- ments, and driving styles.
The demonstration also uncovered additional benefits. By enabling a connection between the vehicle and the driver, the smartphone application provided automakers with a valuable tool for managing customer relationships. They could easily issue alerts and recall noti- fications for massive savings in customer notification, as well as an increase in customer satisfaction.
14:25 2/11
75412 km 50
Recall Notification litre/100
you saved 150
6.3 6.8
A possible problem with the car's battery has been detected. To make an appointment, see suggested time slots or contact your dealer.
Please, find a slot I will call myself
Seeking to explore variations in driver behavior, Altran and Microsoft extended this open-and-connected car platform with Microsoft Azure cloud services. Machine learning algorithms classified driver fuel con- sumption by demographic characteristics, building a predictive model for forecasting fleet-level or regional fuel consumption. When com- bined with forecasted fuel cost variations, the model could advise on cost-optimized individual refueling schedules. This predictive element strengthens the connection between automaker and driver. Collecting data on additional parameters allows automakers to offer service spe- cials, new car rebates, and other timely communications based on a car owner’s actual vehicle use.
you have no alerts no maintenence you have no alerts no maintenence
Handling Big Data A key insight from this demonstration was the technical trade-off required between the richness of the sensor suite and the cost and feasibility of processing the resulting volumes of data in the cloud. The early objective to “capture everything” was soon scaled back in consideration of the data volumes generated by a 1 Mbps CAN bus on each of 500,000 vehicles.
Figure 2.
Drivers’ smartphones provide access to an application displaying vehicle information.
Network bandwidth and cloud costs will affect the volume of infor- mation automakers collect and analyze in the cloud. They can, how- ever, balance the cost of data transfer and storage against the cost of providing local processing and intelligence. Strategies for reducing the communication cost include aggregating several sensor values into a composite measure and reporting some parameters only when they deviate from a specified range. Also, increasing the sophistication of the in-vehicle data pro- cessing can help reduce lifetime data management costs.
Intel® In-Vehicle Solutions Figure 3. 16 | 2016 |
Automakers can view aggregated usage, advisory information, and key performance indicators.
13th Edition | Embedded Innovator |
intel.com/embedded-innovator
under the Hood A more advanced vehicle platform than the demonstration platform is the Intel In-Vehicle Solutions development kit. This platform provides the compute power to handle VueForge, in-vehicle infotain- ment (IVI) systems, and on-board sensors
Page 1 |
Page 2 |
Page 3 |
Page 4 |
Page 5 |
Page 6 |
Page 7 |
Page 8 |
Page 9 |
Page 10 |
Page 11 |
Page 12 |
Page 13 |
Page 14 |
Page 15 |
Page 16 |
Page 17 |
Page 18 |
Page 19 |
Page 20 |
Page 21 |
Page 22 |
Page 23 |
Page 24 |
Page 25 |
Page 26 |
Page 27 |
Page 28 |
Page 29 |
Page 30 |
Page 31 |
Page 32 |
Page 33 |
Page 34 |
Page 35 |
Page 36 |
Page 37 |
Page 38 |
Page 39 |
Page 40 |
Page 41 |
Page 42 |
Page 43 |
Page 44 |
Page 45 |
Page 46 |
Page 47 |
Page 48 |
Page 49 |
Page 50 |
Page 51 |
Page 52 |
Page 53 |
Page 54 |
Page 55 |
Page 56 |
Page 57 |
Page 58 |
Page 59 |
Page 60 |
Page 61 |
Page 62 |
Page 63 |
Page 64 |
Page 65 |
Page 66 |
Page 67 |
Page 68
