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Design of Cooperative Vehicle Safety Systems Based on Tight

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					       Design of Cooperative Vehicle Safety Systems
                          Based on
       Tight Coupling of Communication, Computing
                            and
                Physical Vehicle Dynamics
Yaser P. Fallah,              ChingLing Huang,              Raja Sengupta,                Hariharan Krishnan
Univ of California, Berkley   Univ of California, Berkley   Univ of California, Berkley   General Motors R&D




                                                                                          Presented by
                                                                                          Rohit Nampelli
Index
•   Abstract
•   Introduction
•   Existing Knowledge on CVS
•   DSRC Bases CVS System (Existing architecture)
•   Tightly Coupled CVS System (Proposed System)
•   Component Modeling: Computation on Communication
•   Component Modeling: Physical process Estimation
•   CPS Component Interaction: Tightly Coupled Design.
•   Experimental Evaluation
•   Conclusions
Abstract
• CPS: Computing, Communication, Physical Dynamics.

• CVS: Vehicles broadcast their physical state information, so
  their neighbors can track and predict possible collisions.

• Physical Dynamics of vehicles -> Required accuracy for tracking
  -> Load on the network -> Network performance.

• The tight mutual dependence of these factors require the
  system to be tightly coupled.

• We design a tightly coupled system and compare it with
  systems with independently developed subcomponents.
Introduction
• A Cooperative Vehicle Safety (CVS) systems deliver warning
  messages to driver / directly take control of the vehicle.

• Cyber component: Detection of threats ,Transmit safety
  messages.

• Existing models don’t consider the relation between network
  load, tracking process, effect of physical dynamics, estimation
  accuracy.

• By coupling the design of the cyber component with the
  components related to vehicle dynamics we can gain
  significant performance improvement.
Existing knowledge on CVS
• Latency of warnings
   • Active safety systems (Collision Avoidance – low latency)
   • Situational awareness (Heads up info of non immediate dangers)
• Active safety systems – Dedicated Short Range Communication
  (DSRC) channels. Low Latencies of few hundred ms.




                Fig 1: V2V CVS Communication using DSRC
Existing knowledge on CVS
• Situational awareness : 30 –
  60 Secs ahead of the vehicle.

• Due to high latencies of
  present communication
  technologies, they can only
  function for situational
  awareness but not for Active
  safety systems.

• Ex: warning about a traffic
                                  Fig 2: Network Traveler Soft Safety warning system
  queue at a road curve.
DSRC based Cooperative Vehicle Safety Systems

• DSRC based CVS has 2 types of safety
  messages
   • Event driven emergency messages
     (High Priority)
   • Frequent vehicle tracking
     messages (Low priority)

• Vehicle Tracking Messages : Include
  vehicle location, speed. Used to track
  neighboring cars. (Tracking has to be
  accurate)

• In high traffic, DSRC channel is easily
  saturated.
Tightly Coupled CVS System
Tightly Coupled CVS System
• Estimate the physical process (location, speed) in
  computing module.
• Traditional systems samples this state and broadcasts at
  100msec intervals.
• Instead, use a model based estimator
• Constant speed model: Vehicle speed remains constant
  between sampling times.
• Each CVS device has a bank of estimators for the vehicles it
  is tracking.
• Sender runs a local estimator of its own position using the
  same model that is used at remote estimators.
• If the estimate is found to have large error when compared
  with its actual position, transmission logic broadcasts a
  new message to the other cars.
CPS Component Modeling: Effect of Computation/ Physical
Processes on Communication
• Understand the relation between the Computation/physical
  process and communication module.
• Transmission control logic (Fig 4) controls parameters in
  communication module allowing optimal performance.
• Communication process parameters: Packet frequency, length,
  power, MAC layer settings.
• Few of them being predetermined, Packet Rate, Power Level
  are only controllable.
• Performance Metric: Information Dissemination Rate (IDR) /
  Broadcast Throughput -> No of sender packets received at the
  receivers.
• Simulation of the VANET and observed IDR for various
  Transmission Rates (R), Ranges (D).
Effect of Computation/ Physical Processes on Communication

For a given values of rate R and traffic density ρ, there exists a value of D which
Yields maximum IDR.
For a selected R, an optimal operation can be reached by varying the D value.




                                                        Figure 6 5 Information Dissemination Rate vs. range of
 Figure 5 Information Dissemination Rate vs. range of
                                                                       transmission for different
               transmission for different
                                                                        transmission rates, ρ=.2
                transmission rates, ρ=.1
Effect of Computation/ Physical Processes on Communication

 • Channel Occupancy (U) can be used as network feedback which is used for
 controlling the communication component.




   Figure 7 The effect of transmission range (D) and rate   Figure 8 IDR vs. channel occupancy for different values
           (R) choices on channel occupancy (U)                of R(5-115 msg/sec), D(20-400m), and ρ (0.1-0.2
                                                                             vehicle/m)Relationship



 In the relation between IDR and U, For different values of R, D, ρ it can be
 observed that all the IDR values fall on a single curve which means that IDR and
  U are related.
 It means that a controller must be designed to run at an optimal channel occupancy
 Where IDR is maximum. (in this case, channel occupancy is 0.6)
CPS Component Modeling: Computing module
and physical process estimation
• Accuracy levels depend on the rate of message transmission.
  Faster moving cars need to transmit messages at a higher rate.
• Effect of Physical process / communication performance
  (message rate) on computing performance (tracking accuracy).
• Message rate : Rate of successful reception of messages
  (transmission rate x success probability).
• Packet transmission is varied by either
  • Probabilistic policy
  • Error dependent policy
• Message Accuracy is defined in 2 ways
  • MSE: Mean Square Error
  • 95% cut-off error
Computing module and physical process estimation

 • 95% cut-off error is the value below which 95% of the error histogram lies.
 • Rate of transmission in probabilistic policy is controlled by changing the
   probability of transmission.
 • Rate of transmission for Error dependent policy is controlled by adjusting
   an error sensitivity parameter α.
 • The error rate drops quicker in case of error dependent policy.
 • The error saturates at a point. At that point, the network must be used to
   reach the farther nodes.
   CPS Component Interaction and Tightly
            Coupled Design
• Communication subcomponent can control the Range of
  transmission by setting the power level and provides feedback
  on the measured channel occupancy.
• The objective here is to design algorithms that control the
  rate, R, and range, D, of transmission based on the observed
  network feedback U, and perceived tracking error e.
• Tracking accuracy (data delivered to the receivers) is related to
  rate of transmission (R). R can be controlled by varying the
  Range of transmission (D).
• So for crowded networks, we reduce the range in increase the
  accuracy. Vice versa, for sparse networks we increase the
  range.
CPS Component Interaction and Tightly Coupled Design

Range control algorithm
• Controller must maintain U
   between Umin = 0.4 and Umax
   = 0.8.
        Evaluation                                Case           Direction 1
                                                                 Status
                                                                               Direction 1
                                                                               Speed
                                                                                             Direction 2
                                                                                             Status
                                                                                                           Direction 2
                                                                                                           Speed
                                                  H1             Congested     14mph         Congested     14mph

                                                  H2             Low Speed     30mph         Low speed     30mph


                                                  M1             Congested     14mph         Free flow     74mph

                                                  M2             Low Speed     30mph         Free flow     74mph




Figure 12 OPNET and SHIFT simulation results for different
traffic scenarios, the proposed range control scheme vs. fixed
range.
Conclusions
• We have seen the interaction and mutual effects of different
  components of the CVS.
• Tight coupling of computing, communication and physical
  dynamics of the CVS have been observed.
• We have observed that the tight coupling of the CPS
  components increases the performance of the CVS.
• With availability of micro level models of communication and
  computing, the proposed method can still be improved.
Queries ?

				
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posted:4/1/2013
language:English
pages:19