NXP - 从云端到车端.pptx
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1、,NXP and the NXP logo are trademarks of NXP B.V. All other product or service names are the property of their respective owners. 2017 NXP B.V. PUBLIC,DR. RAJEEV ROY, NXP MICHAEL JOHNSTON, NXP SEPTEMBER 24TH, 2019,FROM THE CLOUD TO THE CAR: THE END TO END PICTURE,AGENDA,PUBLIC,1,Market trends Use cas
2、es Network evolution and landscape Security considerations Vehicle telematics Summary,Automotive Industry Megatrends,PUBLIC,2,PUBLIC,3,CONNECTED VEHICLES 38 MILLION Shipped in 2018*,Sources: *Strategy Analytics, 2019; *ABI Research, 2018,V E H I C L E B I GD ATA o p p o r t u n i t i e s,Represents
3、global data,VEHICLE DATA 4+ TERABYTES Vehicle data generated per hour*,Connected vehicle penetration*,Vehicle Data Opportunities Will Transform the Automotive Industry,New Revenue Streams Up to $750B* for data-driven services by 2030 77.4% millennials* willing to pay for updates Enhanced Safety and
4、Security Fault detection & notification Intrusion detection and prevention Crash detection / emergency response Improved User Experiences Personalization, comfort and convenience Post-sale feature upgrades Location-based services Reduced Costs Predictive maintenance Reduced warranty / recall exposur
5、e Fleet management,PUBLIC,4,Sources: McKinsey IHS Markit forecast, 2018,Use cases,PUBLIC,5,Over-The-Air (OTA) use case,Over-the-Air firmware/software updates is a key trend in the industry Trend to move OTA Management function in Gateway ECU Centralized management of OTA deployment in-vehicle Interf
6、ace to OEM servers Security is paramount Utilizing OTA mechanism to deploy new features via SW in field (Agile SW deployment) Build performance overhead into hardware In-field, test & deploy new customer features as use cases emerge,PUBLIC,6,Automotive SDN use case,Central configuration of data-plan
7、e switches in IVN Support both TSN and BE traffic Support switching and routing Use cases: Dynamic network changes E.g. Adding new services via OTA while maintaining optimum network efficiency Error handling E.g. Fast failover,NETCONF CNC example:,Data path,PUBLIC,7,Analytics Typical use cases (1),P
8、redictive maintenance Identify vehicle maintenance issues before they occur Schedule maintenance when needed Quickly identify the root cause of any problem Machine Learning (ML) at the edge to detect safety relevant maintenance issues Intrusion Detection and Prevention (IDPS) Uses anomaly detection
9、techniques to identify malicious activity or faults on vehicle networks Detects static anomalies (non ML) Contextual anomaly ML based ML at the edge to detect attacks in real time,PUBLIC,8,Crash detection Used to detect if a crash occurs Indicates the severity of crash Indicates likelihood of seriou
10、s injury Capture moments before crash ML at the edge to detect crash real time,Usage based insurance Behaviour policy pricing Premium based on driver use of vehicle ML determine driver premium depending on several risk factors ML at the edge to detect driver and usage,PUBLIC,9,Analytics Typical use
11、cases (2),Cloud Predictive Data Analytics,Analytics: Handling Data,On-Vehicle processing Capture: Data to service (raw data to speed, temp, etc) Data logging Process: Predictive data analytics (limited) Deviation detection Signal correlation, comparison to reference modules Aggregate Diagnostic Trou
12、ble Codes (DTC) with other data and time stamps Share: Compression before offload,Off-Vehicle Cloud processing Correlation of data across multiple vehicles Advanced predictions using historical and warranty data Part defect history,SOC,Network Services,On board data streams,On board data streams (se
13、nsor data),Repair history,Aggregated vehicle data,Model data,Model data (limited),Off-Vehicle,On-Vehicle,Predictive Data Analytics,Share,Process,Capture,Hardware support to analyse network streams,Heavy math vs Decision Tree,Optimize available bandwidth,PUBLIC,10,Network evolution and landscape,PUBL
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