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
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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
queue at a road curve.
Fig 2: Network Traveler Soft Safety warning system
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 5 Information Dissemination Rate vs. range of
transmission for different
transmission rates, ρ=.1
Figure 6 5 Information Dissemination Rate vs. range of
transmission for different
transmission rates, ρ=.2
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
• 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 ?