Efficient RealTime Support for Automotive Applications: A Case Study

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Efficient Real-Time Support for Automotive
Applications A Case Study
Gurulingesh R.
Gurulingesh R., Neera Sharma , Krithi
Ramamritham, Sachitanand Malewar Indian
Institute of Technology Bombay INDIA
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Motivation(1/2)

• Rapid increase of computer-controlled functions
  in automotive applications
• e.g. New Mercedes S-class cars employ atleast
  70 networked ECUs
• Independent black-box implementation is
  practically infeasible
• Cost, Integration Complexity

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Motivation(2/2)

• Safety-critical applications
• Brake-by-Wire, Collision Avoidance system,
  Adaptive Cruise Control, etc
• deal with critical data and deadline bound
  computations
• have stringent requirements on
• Freshness of data
• Completion time of tasks

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Introduction to ACC (1/2)

• Adaptive Cruise Control tries to maintain
• Safe Distance when there is a leading vehicle
• Set Speed when there is no leading vehicle in
  its path

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Introduction to ACC (2/2)

• Extension of Cruise Control.
• Operates either in
• Distance Control state
• Speed Control state

Des_Dist Host_Vel Timegap ?
where Host_Vel is Host Vehicle
velocity TimeGap is set by the driver ? for
additional safety
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Focus of Our Work

• Efficient utilization of computing resources
  while satisfying safety-critical properties

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Issues (1/3)

• Effective tracking of dynamically varying data.

General Practice Prepare for the Worst
Over-Sampling
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Issues (2/3)

• Timely updates of derived data

General Practice Periodic updates
Unnecessary Updates
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Issues (3/3)

• Some tasks will execute only in some modes

• Adapt parameters when lead car is far
• Sense adjacent lane, time to collision when car
  is near

General Practice Single mode design for
simplicity
Poor CPU utilization
Scheduling Overhead
Not modular
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Our Approach(1/3)

• Dual Mode System
• Two mutually exclusive phases of operation
• Safety Critical Mode
• Non Safety Critical Mode
• Current mode depends on
• Distance of Separation
• Rate of change of Distance

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Our Approach(2/3)

• Real-Time Data Repository
• Two level data store
• Environment Data Repository
• Derived Data Repository

• Task Scheduling
• Constant Bandwidth Server (CBS)

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Our Approach(3/3)

• Real-Time Data Repository

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Robotic Vehicle Experimental Setup

• Capabilities
• Obstacle detection Range
• 2m
• Maximum speed
• 0.50 cm/s
• White-line following

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Results Observations

• Basic Experiments

• Cruise Control
• Set Speed 25 cm/s

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Results Observations (cont)
2. ACC Varying Velocity - Velocity Response

• Basic Experiments

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Results Observations (cont)
2. ACC Varying Velocity - TimeGap

• Basic Experiments

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Results Observations (cont)

• Basic Experiments
• 1. Cruise Control
• Set Speed 25 cm/s

• 2. Adaptive Cruise Control
• Varying Velocity

• ACC tries to maintain
• Set speed when there is no leading vehicle
• Safe Distance when there is leading vehicle
• Variation in graphs due to Shaft Encoder error

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Results Observations (cont)

• Real-Time Data Repository Experiments
• Task under observation DistT (which updates DoS)
• Threshold Value 5cm
• Leading vehicle with uniform speed

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Results Observations (cont)

• Real-Time Data Repository Experiments
• Task under observation DistT (which derives DoS)
• Threshold Value 5cm
• Leading vehicle with varying speed

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Results Observations (cont)

• Real-Time Data Repository Experiments
• Task under observation DistT (which derives DoS)
• Threshold Value 5cm
• Time Window 0-12 sec

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Results Observations (cont)

• Real-Time Data Repository Experiments
• Task under observation DistT

• Threshold Value 5cm

• Less number of Updates
• Compared to conventional approach
• Efficient usage of computing resource
• Functionality/Safety not affected

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Results Observations (cont)

• Dual Mode Experiment
• Mode change criteria Lead Dist 65 cm/s
• Periodicity of tasks P(SC mode) ½ P(NC mode)

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Results Observations (cont)

• Dual Mode Experiment
• Mode change criteria Lead Dist 65 cm/s
• Periodicity of tasks P(SC mode) ½ P(NC mode)

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Results Observations (cont)

• Dual Mode Experiment
• Mode change criteria
• leading distance 65 cm/s

• Periodicity of Tasks
• P(SC Mode) ½ P(NC Mode)

• Compared to conventional approach
• Efficient usage of computing resource
• Functionality/Safety not affected
• Conservative Approach while deciding SafeDist

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Contributions

• Presented issues involved in developing real-time
  support for ACC
• Efficiently utilized processor capacity by
  designing ACC using following concepts
• Mode change
• Real-time data repository
• Provided scheduling strategies to meet timing
  requirements

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Ongoing Work

• More analysis of the system design (mode-change
  criteria, threshold values, etc.)
• Application needs are being mapped to distributed
  platform
• Study of controllers stability and performance
• Usage of communication protocols such as CAN or
  TTP

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References

• Petros Ioannou Cheng-Chih Chien. Autonomous
  Intelligent Cruise Control. IEEE Trans. On
  Vehicular Technology, 42(4)657-672, 1993.
• Thomas Gustafsson Jörgen Hansson. Dynamic
  on-demand updating of data in real-time database
  systems.
• In Proceedings of ACM SAC 2004.
• Gerhard Fohler Flexibility in Statically
  Scheduling Real-Time Systems. PhD Thesis,
  Technischen Universitat Wien Austria, Apr. 1994.
• L. Sha R. Rajkumar J. Lehoczky K. Ramamritham.
  Mode Change Protocols for Priority-Driven
  Preemptive Scheduling. In Journal of Real Time
  Systems, 1(3)243-265, Dec 1989.

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THANK YOU
Embedded Real-Time Systems Group Indian Institute
of Technology Bombay INDIA http//www.it.iitb.ac.i
n/car/