Performance Analysis of FlexRay-based ECU Networks Andrei Hagiescu, Unmesh D. Bordoloi, Samarjit Chakraborty Department of Computer Science, National University of Singapore Prahladavaradan Sampath, P. Vignesh V. Ganesan, S. Ramesh General Motors R&D – India Science Laboratory, Bangalore

 In a high-end car there are upto 70 ECUs exchanging upto 2500 signals.  Commonly used protocols include CAN, LIN, J1850 etc.  These can be broadly divided into event triggered and time triggered protocols – each has certain advantages and dis-advantages.  Lot of emphasis on hybrid protocols – FlexRay.  FlexRay is also backed by major automotive companies and hence, there has been a lot of interest in performance analysis of FlexRay-based designs. Performance Analysis of FlexRay-based ECU Networks

FlexRay-based ECU Networks  Tasks have different activation rates and execution demands  Each computation/communication element has a different scheduling/arbitration policy ECU FlexRay Bus ECU Comm. Controller Round Robin Fixed Priority EDF Input Events Output Events  Timing Properties?  End-to-end delay?  Buffer requirements?

Abstract Models for Performance Analysis Processor Task Input Data/Events Service Model Event Model Concrete Instance Abstract Representation Processing Model

t [ms] events Arrival Pattern maximum/minimum number of events in any interval of length 2.5 ms 2.5 Arrival Curves [  l,  u ] events uu  [ms] ll 2.5 number of events in t=[ ] ms slide window and record max and min  l (  ) <= R(t+  ) – R(t) <=  u (  ) Event Model – Modeling Execution Requirements t 

Service Model – Modeling Resource Availability t [ms] availability Resource Availability maximum/minimum available service in any interval of length 2.5 ms available service in t=[ ] ms 2.5 Service Curves [  l,  u ] service uu  [ms] ll 2.5  l (  ) <= S(t+  ) – S(t) <=  u (  ) t 

remaining supply processed events Processing Model Service Model Event Model Process ing Model

PE 1 PE 2 Modeling dependency Compositional Schedulability/Timing Analysis Compositional Analysis

Brake Control TDMA Object Detection Object Detection Radar 1Radar 2 ECU2 Data Fusion Object Selection Adaptive Cruise Control Throttle and Brake Torque Arbitration Actuators Path Estimator Throttle Control Wheel Sensor ECU 4 Anti-lock Braking System Sensor (to crash control subsystem) 11 22 33 TfTf BfBf ’’ FlexRay Bus Task dependencies DYN message ST message ECU1 Fixed Priority ECU3 Fixed Priority m1m1 m2m2 m3m3 m4m4 m5m5 m6m6 m7m7 An adaptive cruise control application Case Study – An ACC Application

Performance Analysis  Minimum end-to-end delay when DYN segment length = 9ms and ST segment length = 5 ms  Variations in the end-to-end delay with different sampling periods (ST= 8 ms and DYN = 6 ms) Delay (ms) ST Length (ms) DYN Length (ms) Delay (ms) Radar Period (ms) Wheel Sensor Period (ms) bus cycle length = 14 ms

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