Design of Cooperative Vehicle Safety Systems Based on
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Yaser P. Fallah , ChingLing Huang, Raja Sengupta , Hariharan Krishnan PowerPoint PPT Presentation


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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

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Yaser P. Fallah , ChingLing Huang, Raja Sengupta , Hariharan Krishnan

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Yaser p fallah chingling huang raja sengupta hariharan krishnan

Design of Cooperative Vehicle Safety Systems Based onTight Coupling of Communication, Computing andPhysical Vehicle Dynamics

Yaser P. Fallah, ChingLingHuang, Raja Sengupta,Hariharan Krishnan

Univ of California, BerkleyUniv of California, BerkleyUniv of California, BerkleyGeneral Motors R&D

Presented by

RohitNampelli


Index

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

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

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

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 cvs1

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 queue at a road curve.

Fig 2: Network Traveler Soft Safety warning system


Dsrc based cooperative vehicle safety systems

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


Tightly coupled cvs system1

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

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

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 5 Information Dissemination Rate vs. range of transmission for different

transmission rates, ρ=.1

Figure 6 5 Information Dissemination Ratevs. range of transmission for different

transmission rates, ρ=.2


Effect of computation physical processes on communication1

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 (R) choices on channel occupancy (U)

Figure 8 IDR vs. channel occupancy for different values 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

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

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

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 design1

CPS Component Interaction and Tightly Coupled Design

Range control algorithm

Controller must maintain U between Umin = 0.4 and Umax= 0.8.


Evaluation

Evaluation

Figure 12 OPNET and SHIFT simulation results for different

traffic scenarios, the proposed range control scheme vs. fixed

range.


Conclusions

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.


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