Creating a Safer, Smarter ride - NFV for Automotive
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While NFV and SDN have showcases their potential in cloud Data centers, experts are looking to bring its expertise for creating a secured safer smart ride through the integration of vehicle-vehicle and vehicle-infrastructure communications which create smart locales. Today we have understood the requirements and networking involved to realize centralized and distributed clouds to support customer premise services and IIoT. But we have a partial gain from these technologies. To unlock the real potential of Edge networks, the Automotive industry is moving towards integrating ADAS and intelligent roadside infrastructure with Cloud Edge and NFV technologies to create a Safer and Smarter Ride. This presentation showcases on NFV for Automotive to create safer and smart ride.
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Creating a Safer, Smarter ride - NFV for Automotive
- 1. NXP DIGITAL NETWORKING GROUP STEVE FURR, TRINATH SOMANCHI, NELSON YANG CREATING A SAFER, SMARTER RIDE – NFV FOR AUTOMOTIVE MARCH, 2018
- 2. 1 AGENDA • Motivation • Major Causes of Collisions and Injuries • Communication Needs • Architecture for ITS Edge Computing • Use Cases • NXP Solutions
- 4. 3 Source: National Highway Transportation Safety Administration • In 2016, 37,461 people died in motor vehicle crashes • Costing > $650B annually • Motor vehicle accidents are the leading cause of death among youth and young adults 16-24 • The vast number of vehicle crashes are tied to human error • Everyone is a pedestrian • On average, a pedestrian is killed every two hours and injured every seven minutes in traffic crashes • Fourteen percent of all traffic fatalities and an estimated 3 percent of those are injured in traffic crashes are pedestrians More than 70% of all fatal collisions involve speeding or aggressive driving as a factor Vehicle Safety Pedestrian Safety Motivation – Improve Traffic Safety
- 5. 4 Sources: Federal Highway Administration, American Society of Civil Engineers, EU 5G Public-private partnership (5G-PPP) Capability trap - need to achieve more with existing (deteriorating) road infrastructure • Road capacity and quality levels haven’t improved significantly in several years • Rehabilitation and maintenance have expenditures increasing at exponential rates • Government agencies in difficult financial situation asking for more resources • No room or desire to build out roadways Increase capacity of existing roadways • Network density and scale – 5G network infrastructure will see: − 1,000x increase in mobile data volume per geographical area ≥ 10 Tb/s/km2 − 1,000x increase device density ≥ 10 1M/km2 − 1/5x end-to-end latency, reaching target ≤ 1ms for vehicle-to-vehicle communications − Accuracy of outdoor terminal location < 1m • Limited availability of suitable sites for basestation placement to achieve density Public-private partnerships for 5G network infrastructure leverage disruption to revolutionize ITS by locating edge applications at private facilities in the public realm Transportation Infrastructure Gap 5G Network Infrastructure Motivation – Synergize PPPs for 5G Build-out
- 6. 5 02. Major Causes of Collisions and Injuries
- 7. 6 Proximate Causes of Fatal Crashes Driving on wrong side of road Careless driving Operating with improper equipment Improper turn Failure to yield Overcorrection Failure to adjust to conditions Failure to keep to proper lane Alert drivers to the bad behaviors causing fatal crashes Source: AutoInsurance Center
- 8. 7 Roadmap to Autonomous Passenger Vehicles • Roadmap to fully autonomous operation is more than a decade • Autonomous vehicles will continue to share the road with “legacy” users for a long period Source: European Road Transport Research Advisory Council Intelligent transportation will rely heavily on “assistive” technologies enhancing situational awareness
- 10. 9 Communications Needs Vehicle-to-pedestrian (V2P) e.g. pedestrian in intersection Vehicle-to-Network (V2N) e.g. mapping, traffic conditions Vehicle-to-anything (V2X) Vehicle-to-infrastructure (V2I) e.g. signal conditions (light changing, pedestrian in crossing, etc.) Vehicle-to-vehicle (V2V) e.g. emergency vehicle approaching, signaling intent (lane change, etc.)
- 11. 10 Vehicle External Communications External Comms • GNSS navigation • Traffic advisory services • Owner support services • Passenger entertainment External Comms • Assist + • Low volume, low latency V2V • Low volume, low latency V2I External Comms • Co-pilot + • Mid volume, low latency V2V • Mid volume, low latency V2I, V2N • Mid volume map data Increasing Complexity Increasing Safety Adviser Co-Pilot Chauffeur
- 12. 11 Connected & Automated Vehicle (CAV) Communications Needs Vehicle to Vehicle (V2V) • Short range communications • Allows similarly equipped vehicles to signal intent − Wireless turn signal − Wireless brake light − Intent to merge but remain in lane − Intent to merge into your lane − Relay emergency braking message from vehicles ahead • Without V2V, CAVs must drive very conservatively. • Vehicle to Infrastructure (V2I) • DSRC (short range) or Cellular (short & long) range communications • Helps CAV and legacy vehicles navigate complex environments more smoothly − Intelligent traffic lights; flow control based on volume − Supplemental navigation signals − Merger of Tolls with prioritized routing • High security and QoS requirements to isolate safety messages from high bandwidth non-navigational infotainment
- 13. 12 Use Cases Click to edit Master text styles Aenean nec lorem. In porttitor. Donec laoreet nonummy augue. Suspendisse dui purus, scelerisque at, vulputate vitae, pretium mattis, 04.
- 14. 13 Forward Collision Prevention Preventing Detectable Collisions Forward Collision Warning Detects a potential collision and warns the driver Hard Braking Ahead Detects an obstruction and warns the driver, automatically reducing speed Automated Emergency Braking Applies brakes when forward collision imminent Collision Avoidance Detects an imminent collision and navigates to avoid Pedestrian Automated Emergency Braking Detects pedestrian, warns driver and automatically brakes if collision imminent Lateral Threat Detection Warns of: emergency vehicle, unyielding vehicle, etc., and automatically brakes if collision imminent Assistive Edge-Enhanced Situational Awareness
- 15. 14 Lane Navigation Navigating Safely Around Other Vehicles Lane Departure Warning Monitors lanes and provides warnings if driver is out of bounds Lane Following Assists driver following lane, including in adverse conditions Lane Keeping Assist Helps driver stay within bounds of lane Aggressive Driver Warning Detects a vehicle speeding or maneuvering aggressively and warns the driver Blind Spot Detection Warns of vehicle in driver’s blind spot (when turning) Collision Avoidance Detects impending blind spot encroachment and navigates to avoid Assistive Edge-Enhanced Situational Awareness Map (guideposts)
- 16. 15 Safe Separation Maintaining Safe Following Distances Traffic Jam Assist Automatically accelerates and brakes the vehicle with the flow of traffic Convoying / Platooning A convoy of vehicles follows a lead car to achieve efficiency Highway Pilot Maintains vehicle’s lane position and following distance by braking and accelerating as needed Congestion Avoidance Adaptive Cruise Control Automatically adjusts vehicle’s speed to keep a preset distance from the vehicle in front Assistive Edge-Enhanced Situational Awareness reroute
- 17. 16 CLOUD Back Haul to the Cloud Broadband Hotspot Legacy Vehicle Detection Pedestrian (VRU) Detection Obstruction DSRC Relay VRU Warning, Traffic Control Warnings Direct V2V Direct V2V Simple RSU
- 18. 17 Intelligent ‘Slot-Based’ Intersections http://senseable.mit.edu/papers/pdf/20160316_Tachet_etal_RevisitingStreet_PLOS.pdf Photo via senseable.mit.edu
- 19. 18 Intelligent Transportation Services Will Require Edge Computing CAV Base Station Metro EPC Core Network Cloud Server ~115ms round trip, Bulk of latency is in metro/core networks. 5ms 12ms 20ms1ms 10ms 10ms 10ms 10ms 20ms9ms 5ms 1ms 2ms 5ms 12ms1ms 9ms 5ms 1ms 2ms ~35ms round trip 5G targeting 1ms end to end latency CAV Base Station with Edge Computing
- 21. 20 Key Enabling Technologies Cloud • Virtualization • Security (Server) • Virtualization (NFV, AWS GreenGrass) • Near RealTime • Security (Proxy) • Secure Device Management Edge • Security (Client) • Secure Device Management End Nodes SMART HOME SMART VEHICLESSMART INDUSTRYSMART HEALTHCARE
- 22. 21 Distributed Artificial Intelligence Cloud • Big Data Fusion • Training Engines • Inference Engines • Localized Data Aggregation, Information Generation • Inference Engines for data analytics End Nodes Edge • Sensors, Data Generation • Inference Engines for audio/visual recognition
- 23. 22 ETSI Multi-Access Edge Compute Architectural Framework Mobileedge systemlevel Mobile edge system level management UE 3rd party 3GPP network DSRC network External network Mobile edge host level management Mobileedge hostlevel Networks Mobile edge app Mobile edge applications Mobile edge platform Mobile edge host Virtualization Infrastructure (e.g. NFVI) Mobile edge app Mobile edge app Mobile edge app
- 24. 23 NFV - From Data Center to Edge • Future network will connect billions of people, devices and things (IoT) • Distributed model – − Distributed data centers (DCs) − Geographically dispersed micro DCs − Distributed NFV infrastructure – VNFs placed everywhere between central DCs and edge devices − Migration toward multiple hierarchical controller domains • Edge / Aggregation nodes provide path to supporting edge devices − Connect edges to things, including smart vehicles
- 25. 24 Motivations for Virtualization & Types • Efficiency: Consolidation onto fewer processors for higher hardware utilization − Oversubscription tolerated • Ease of management − Create/destroy virtual instances as needed − Migrate running instance to different system • Flexibility − Use different versions of Linux − Run legacy software or OS on HV • Sandboxing– allows untrusted software to be added to a system (e.g. operator applications) Hardware App OS Hardware App OS Hardware App OS App OS Virtualization Layer Linux ® OS App OS App Hardware Hypervisor Linux® KVM RTOS App App Linux® App App Hardware QEMU Linux® Hardware Ap p Ap p App Ap p Ap p App Ap p Ap p App DockerDockerLXC containersGuests
- 27. 26 NXP in Automotive Radar V2X Vision/Fusion eCockpit/Infotainment • 77 GHz proven in production • Programmable chips to 4 GHz • MIPI interface 4x 20 Mbps • Industry’s first RFCMOS Radar SoC in the making • Unique WW RFCMOS Solution • Best-in-class Security • >1 Million Days Field-Tested • First Global OEM Design Award • Next Gen: System optimized Single Antenna 1-chip • Many Core • ASIL B-D • Many Core • GPUs • Radio - AM/FM - Digital (DAB, HD)
- 28. 27 NXP in Networking/Telco Service Provider Wireless & Wired Equipment NXP Digital Networking Market NXP QorIQ processors offer server class performance for real time control and high touch data services in wireless and wireline infrastructure Enterprise / Data Center Network Infrastructure General Embedded Mil/Aero, Industrial, Printing & Imaging Data/Cloud Enterprise Service Provider Multi-access Edge Industrial
- 29. 28 We Care About the “V” and the “I” Vehicle Intelligent Intersection / ITS “Spot” DSRC DSRC Sense – Vision, RADAR, DSRC Rx, GPS Think – Sensor fusion, motion planning Act – Motion control Communicate – DSRC Tx, Broadband Infotainment Sense – Vision, RADAR, DSRC Rx, Wide Area sensors from Cloud Think – Sensor fusion, flow optimization, anomaly detection, motion planning Act – Traffic light control, DSRC traffic control message, Avoidance directives Communicate – DSRC Tx, Broadband Infotainment Hotspot, Analytics data to Cloud
- 30. 29 Intelligent Traffic Control/RSU Proof of Concept System Cohda MK5 DSRC Antenna (vector) Processing PCIe “Beige Box” Sensor Processing Automotive Multicore SoC PCIe CPRI 5G Baseband Processing RF XCVR 5G Radio Head Gb Ethernet Backhaul Radar1 Radar2 Radar3 Radar4 Camera1 Camera2 Camera3 Camera4 L2 Switch Gb Ethernet 10Gb Ethernet iHigh Perf Multicore SoC VM1: Base Station VM2: Traffic Sensing & Control VM4 Control Processing Datapath Packet Processing Web cache, media server Traffic Stats Reporting VM3 Plotting & Tracking Traffic Light Control V2X Message Gen
- 31. 30 Roadside Unit (RSU) and Virtual RSU RRC MAC RLC PDCP IPSEC IPv4/v6 UDP GTP Signaling / STCP Payload Operation & Maintenance Linux Base Station Applications 802.11p (DSRC) L1-2 802.3 (Ethernet) L1-2 Basic RSU Cellular L1-2 802.11p (DSRC) L1-2 802.3 (Ethernet) L1-2 802.11ac (WiFi) L1-2 RTOS RSU Applications DSRC with Safety Sub-Layer (IEEE 1609.3)IPv6 TCP/UDP Traffic event, statistics reporting Traffic Advisory BSM Messaging VSL RTOS RSU Applications DSRC with Safety Sub-Layer (IEEE 1609.3)IPv6 TCP/UDP Traffic event, statistics reporting Traffic Advisory BSM Messaging VSL Hypervisor
- 32. 31 BeigeBox External & Internal Views
- 33. 32 Summary • Safety, security and infrastructure investments are prime drivers for assistive technologies and intelligent transportation • ITS infrastructure most cope with co-existence of autonomous vehicles and legacy users for a protracted period of time • ITS opens up expansion of existing driver assistance use cases • ITS applications demand high levels of edge computing • Successful delivery of ITS assistive use cases will demand inferencing capabilities • ITS assistance will be a major consumer and driver of edge networking • NXP Semiconductors has a long history of delivering reliable, secure solutions to the automotive and networking markets
- 34. NXP and the NXP logo are trademarks of NXP B.V. All other product or service names are the property of their respective owners. © 2018 NXP B.V.
Hinweis der Redaktion
- Quallity of life issue --- we don’t want to lay down more concrete; merge first two bullets (alternative is stacking, tunneling, etc.) 1,000 X in mobile data volume per geographical area reaching a target ≥ 10 Tb/s/km2 1,000 X in number of connected devices reaching a density ≥ 1M terminals/km2 100 X in user data rate reaching a peak terminal data rate ≥ 10Gb/s 1/10 X in energy consumption compared to 2010 1/5 X in end-to-end latency 4 reaching 5 ms for e.g. tactile Internet and radio link latency reaching a target ≤ 1 ms for e.g. Vehicle to Vehicle communication 1/5 X in network management OPEX 1/1,000 X in service deployment time reaching a complete deployment in ≤ 90 minutes 3 4 End-to-End latency should be understood as limited for the case of terminals physically close, as nearby vehicles, a swarm of robots in an automated factory, or a terminal connecting to advanced services provided by a cloud located within its backhaul. 8
- You don’t have to use this one, just reinforced the idea that as a vehicle gets more autonomous, it will generate and receive more external data to help smooth its driving and optimize traffic flow.
- Intelligent infrastructure works hand in hand with increasingly automated vehicles. Just as the cars drive more smoothly when they’re communicating with each other, they drive more smoothly when they’re communicating with the traffic lights and other roadside equipment.
- The complementary nature of DSRC & 5G illustrated. The infrastructure is able to provide wireless hotspot capability, traffic control, and safety, with reduced wait times at intersections. Cars will be allowed through the intersection unless there is a conflict (including pedestrians). In 5G with massive mimo antennas, there is a requirement for densification. Many smaller base stations, closer to the users. It is logical for the base stations to want to take advantage of power and connectivity provided for traffic control. We believe that traffic control infrastructure and cellular infrastructure will start to merge.
- The latency advantage of edge computing… Overall latency between server and UE: ~78 ms UE application delivery latency: 1 ms eNB<->UE latency: 5 ms eNB latency: 12 ms Transport network termination (NAPI/fastpath stack): 3 ms Scheduling/queueing (femto scenario): 4 ms L2/L1 (10% HARQ): 5 ms CSP latency: 20 ms Estimation based on publically available numbers Internet latency: 20 ms Estimation based on publically available numbers Bulk of latency is in metro/core networks. Co-location of VNF in eNB removes this
- We understand the complementary nature of 5G and DSRC because we play in both.