Algorithms for Collaborating Intelligent Vehicles Krithi Ramamritham

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Algorithms for Collaborating Intelligent
Vehicles Krithi Ramamritham

• with Gurulingesh R, Vipul Shingde, Ashish Gudhe,
  
• Neera Sharma, Jatin Bharadia, Sachitanand
  Malewar
• Embedded Real-Time Systems Lab
• IIT Bombay

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Talk Outline

• Motivation
• Focus of our Work
• Automatic Merge Control (AMC) System
• Our approaches
• Optimization Formulation
• Head of Lane Approach
• Observations
• Adaptive Cruise Control (ACC)
• Our approaches
• Dual-mode system
• Two-level repository
• Observations
• Conclusions Further Work

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Motivation

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

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Motivation

• Modern vehicles are becoming intelligent with
• Onboard sensors, Computational Communication
  Resources,
• ( road-side sensors)
• Increase in safety-enhancing applications
• Brake-by-Wire, Collision Avoidance system,
  Adaptive Cruise Control, Intersection Merge
  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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Focus of Our Work

• Efficient utilization of computing resources
• Ensuring safety comfort to the driver
• Providing real-time guarantees
• Proof of concept using robotic vehicular
  platforms built in our lab

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Automatic Merge Control

• Ensure safe vehicle maneuver at 2-road
  intersections
• Extended to n-road
• Primary Aim
• Safety
• Secondary Aim
• Throughput ?
• Fuel Efficiency ?
• Latency ?

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AMC System

• Vehicles with
• Autonomous driving capability
• Communication ability
• Determining merge sequence
• Decentralized by individual vehicles depending
  on environment data
• Centralized by road-side infrastructure node
• Keeps track of Vehicle Profiles
• Determine
• Merge Sequence
• Future Behavior velocity, acceleration
• Communicate with vehicles

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Optimization Formulation

• Formulated as non-linear optimization
• Minimize average Driver Time To Intersection
  (DTTI)
• Minimize f
• Maximize throughput
• Minimize f
• Constraints
• Precedence Constraint
• Mutual Exclusion within Intersection
• Bound on Vehicle Velocity and Acceleration
• Safety Criteria (Safety distance S)

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Optimization Formulation
System Input ?i,j dij ,uij ,S System Output
?i,j tij

• Using tij, we determine
• Merge sequence
• Future Behavior of vehicles
• aij - Acceleration (constant) of each vehicle
• vij(t) - Velocity of each vehicle
• Acceleration commands are then sent to respective
  vehicles

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Head of Lane (HoL)

• Basic Idea
• Consider head-of-lane vehicle (HoL) in each lane
• Determine which head vehicle should go first
• Consider next set of head vehicles
• Metric Average DTTI
• Accelerate vehicle whenever possible

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HoL Algorithm

• Given current profile and maximum possible
  acceleration, compute when will each head vehicle
  reach the merge region

No
Yes
Vehicles interfere?
Follow the above order and the future behavior
Choose winner based on preference metric
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Merge Order in case of Interference

• Nearest Head
• Allow the vehicle nearest to the merge region to
  go first
• Cascading Effect
• Effect of particular order on the vehicles behind
• Number of vehicles affected
• Net deceleration introduced
• Is HoL Optimal?
• HoL and Optimal have same vehicle order

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Continuous stream of vehicles

• Above algorithms applicable only for a particular
  snapshot
• Issues in extending
• Identifying the snapshot of vehicles
• Determining how often to capture these snapshots
  (i.e., how often to run algorithm)

• Approaches
• Sporadic Approach
• Snapshot All vehicles in AoI
• Sporadicity x/Vmax where
• X - the distance of the closest vehicle outside
  the AoI
• Disadvantages Reconsiders all vehicles (except
  those who have passed through merge region)
  stability concern reconsidering the vehicles
  which are nearer to merge region
• Zonal Partitioning
• Assumption Initially, no vehicles in Zone1
• Apply algorithm to snapshot of vehicles in Zone2
• When vehicles from Zone3 enter Zone2, reapply the
  algorithm without disturbing those vehicles which
  have moved into Zone1

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Implementation

• Optimization formulation implemented in Matlab
• fmincon function
• Execution time
• Variable
• HoL approach with nearest head implemented in C
• Execution time
• Order of 0.1 second

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System Input

• System Parameters
• Amax 4m/s2 Amin -4m/s2
• Vmax 27m/s Vmin 0m/s
• S 5m
• Vehicle Profile at t0

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System Output
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Results Observed
Optimization Formulation
HoL Approach
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AMC -- Summary

• Safe maneuvering at intersection using our
  approaches
• Results of HoL are comparable to the optimal
  solution (fmincons may not be global optimum)
• Execution time of HoL very small compared to that
  of Matlab

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AMC -- Ongoing Work

• Extend HoL n-road, cascading effect
• Extend both approaches to deal with continuous
  stream of vehicles multi-zone, sporadic
• Fine tuning vehicle preference, road priority
• Decentralizing both the approaches
• Challenging scenario human automated vehicles
• Develop real-time support for the system and
  demonstrate the concept on robotic vehicular
  platforms

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AMC -- Findings

• Both internal external factors influence the
  application
• Internal vehicle profile, acc/dec capabilities,
  etc
• External other vehicles profiles, location,
  acc/dec capabilities
• Merge region is a shared resource among vehicles
  on different roads
• Separation of responsibilities desired
• Vehicles maintain profile, make decision, etc
• Infrastructure Node provide global info, help
  to make decision, etc

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Introduction to ACC

• 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

• 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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Design Issues

• Effective tracking of dynamically varying data

General Practice Prepare for the Worst
Over-Sampling
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Design Issues

• Timely updates of derived data

General Practice Periodic updates
Unnecessary Updates
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Design Issues

• 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

• 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

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

• Task Scheduling
• Constant Bandwidth Server (CBS)

• Task Scheduling
• Constant Bandwidth Server (CBS)

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Our Approach

• 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
2. ACC Varying Velocity - Velocity Response

• Basic Experiments

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Results Observations
3. ACC Varying Velocity - TimeGap

• Basic Experiments

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Results Observations

• 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

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

Velocity Response
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Results Observations

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

Velocity Response
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Results Observations

• 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

• 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

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

Velocity Response
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Results Observations

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

Timegap Response
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Results Observations

• 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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ACC Conclusions

• 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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ACC Ongoing Work

• More analysis of the system design (mode-change
  criteria, task periodicities, threshold values,
  etc.)
• Application needs are being mapped to distributed
  platform
• Hybrid design dual-mode with data repository
• Study of controllers stability and performance
• Vehicle platooning

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AMC ACC Findings

• Both internal external factors influence the
  application
• Internal vehicle profile, acc/dec capabilities,
  driver set timegap, etc
• External other vehicles profiles, location,
  acc/dec capabilities
• Road is a shared resource among vehicles
• Separation of responsibilities desired
• Vehicles maintain profile, make decision, etc
• Infrastructure Node provide global info, manage
  the shared resource intelligently, etc

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Ongoing Research

• CarOS
• Manager within vehicles to
• Manage processes within vehicles
• Manage resources within vehicles
• Provide networking solutions
• Provide security or protection, etc.
• Example Any OSEK standard OS with extensions
• RoadOS
• Manages the transport infrastructure

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Goals of Ongoing Research

• RoadOS
• Manager of infrastructure
• Manage resources shared by vehicles
• Provide networking solution
• Migration of vehicles, etc.
• RoadDB
• Maintains real-time information
• Tracks vehicle status and road-side
  infrastructure
• Real-Time Continous Query processing
• Effective data dissemination, etc.

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Goals of on-going Research

• Design and Development of
• RoadOS
• RoadDB
• CarOS (extend OSEK standard)
• Interaction between RoadOS CarOS
• Sensor Network Data Management
• Demonstrate on robotic vehicular
• platforms built in our lab.

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References

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Thank You !!
Embedded Real-Time Systems Lab Indian Institute
of Technology Bombay http//www.it.iitb.ac.in/rese
arch/labs/erts_lab/index.php