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Automatic Merge Control Algorithms
Ashish Gudhe
Roll. No. 05305028
Guide :-
Prof. K.
Ramamritham
Roadmap
Aim of this project
Introduction to Automatic Merge Control(AMC)
Existing AMC algorithms
Proposed AMC algorithms
Experiments
Conclusion
Future Work
Video
Reference
Aim of this project
Understand AMC
Primary Aim : Safety at intersection of lanes Secondary Aim :
Increase Traffic Throughput Minimize Time To Merge
Design AMC algorithms. Study performance of the algorithms. Perform experiments on vehicular platforms.
Introduction to AMC system
What is Automatic Merge Control (AMC)?
Automotive Application for automated merging of
vehicles in the intersection region.
Determines merge order of vehicles.
Safety-Critical Application.
Some definitions in AMC system
Time To Merge (TTM):- It is the time required by a
vehicle to reach the intersection region from current position.
Merging Sequence:- It is the order in which vehicles
merge at the intersection.
Area of Interest (AoI):- The area defined by radius R
where all vehicles in this area become part of AMC algorithm.
Safety Distance :- The minimum separation the
vehicles need to maintain at any instant of time.
AMC Algorithms
Existing algorithms
Optimization formulation
Head of Lane Approach (HoL1)
Virtual Vehicle based merging.
Proposed algorithms
Virtual Vehicle based merging.
Head of Lane Approach (HoL2).
All Sequences Minimal Cost (ASMC).
Optimization Formulation[6]
Merging problem is formulated as an optimization problem.
Input : Vehicles’ profiles.
Output: TTM of each vehicle.
Objective function is to minimize the average TTM.
Constraints:-
Precedence Constraint
Mutual Exclusion Constraint
Lower bound on TTM
Drawbacks :
Non-linear constraints. Global optimum not guaranteed.
Computationally intensive.
HoL Approach[7] : HOL1
• Head of lane is the leader vehicle.
• Merge sequence is generated iteratively by inserting selected head vehicle.
• Merging Decision : To select either V11 or V21.– Accelerate both the vehicles to tolerable limits.
– Insert Nearest Head vehicle in case of interference. Choose the next vehicle in the lane as new head of lane.
– Insert the vehicle with smaller TTM and the next vehicle in same lane becomes new head of lane.
Virtual Vehicle based Merging
• Virtual vehicle[4] is an image of actual vehicle mapped on other lane.
• Longitudinal control using Adaptive Cruise Control (ACC)[1].
• The virtual vehicle becomes lead vehicle.
• ACC with virtual vehicle.
• Selection of vehicle as a virtual vehicle depends upon two main criteria :-
• Spatial proximity• Temporal proximity
Virtual Vehicle based Merging
Choice of virtual vehicle
depends on following criteria:-
Spatial Proximity e.g., V2 is mapped as V2’
Temporal Proximity e.g., V1 is mapped as V1’
Virtual vehicle based merging
• Zonal distribution of AoI• Zone 3 monitors the number of
vehicles entering the AoI.• Zones 1 and 2 define the two modes of
system operations (communication, vehicle mapping, ACC, etc.)
• The frequency of operations of vehicles in Zone 1 can be double that of in
Zone 2. • Area based mode change.• Better control expected close to
intersection region. • Zone sizes can be made flexible to
handle traffic of various natures.
All Sequences Minimal Cost (ASMC) Approach
Generates all possible valid merge sequences. Outputs the best merge sequence i.e. one with
minimal merging cost. Recursively computes the valid sequences by
dividing the merging problem with n vehicles to merging problem with (n-1) vehicles.
Guarantees optimal solution. Computationally intensive and space
consuming. Benchmark for comparing performance of other
approaches.
ASMC algorithm short description
where:-• S1 = set of vehicles on lane 1• S2 = set of vehicles on lane 2• refVehicle : reference vehicle w.r.t which behavior of remaining
vehicles is computed.
HoL2 approach
Cascading effect
considered
Merge sequence
generated based upon
effect on the subsequent
vehicles.
Effect in terms of TTM
Effect in terms of deceleration
Effect in terms of number of
vehicles being affected
Extensions to AMC algorithms : Handling continuous streams of
vehicles• AMC algorithms take static snapshot of vehicles
• Merge sequence generated considering this snapshot
• How to handle continuous stream of vehicles entering Area of Interest?– Time based approach where snapshot is taken in regular
intervals of time.– Zonal distribution of Area of Interest where the AoI is
divided into zones and snapshot timing depends on certain criteria.
Zonal distribution of AoI
Z1: Zone 1 closest to
intersection region. Vehicles’ profile remain unchanged in this zone
Z2: Zone 2 of which snapshot
is taken. All vehicles in this zone are part of AMC algorithm.
Z3: Covers entire AoI along
with Z1 and Z2. Farthest zone
which tracks new vehicles that
are about to enter Z2.
Extensions to AMC algorithms cont…
Snapshot timing
After a new vehicle enters zone
Z2
more computations
high prob. of same vehicle
being included in
successive snapshot.
After zone Z2 of any lane
becomes empty :
lesser computations
low prob. of same vehicle
being included in
successive snapshot.
Virtual Vehicle experiment
ACC enabled vehicle VACC
Cruise controlled vehicle VCC
Local learning with position feedback
Initial distance from intersection is fixed :
SACC=1000mm and SCC=500mm.
Current position computed from position
feedback.
Experimental Robotic Vehicles
Virtual Vehicle experiment cont…
Spatial proximity is used as the criteria for
virtual vehicle mapping. Hence, VCC is mapped
as virtual vehicle ahead of VACC.
VCC communicates its current location to VACC
VACC computes the separation distance from the
virtual vehicle.
VACC performs ACC with the virtual vehicle.
Virtual vehicle merging results
Velocity(VACC)=100 mm/sec and Velocity(VCC)=50mm/sec and desired time gap was set to 1sec.
Final velocity of VACC=50mm/sec approx.
The vehicle VACC follows the virtual lead vehicle with time gap between 1 to 1.5 sec.
Virtual vehicle merging results
The initial distances of VACC and VCC are 1000mm and 500mm
respectively from intersection region.
Y-axis denotes the distance from intersection region.
Safety is ensured at the intersection region.
C++ Simulation experiments (case 1)
Vehicle parameters :
Velocity bounds = [0,27] m/s
Acceleration bounds = [-4,4] m/s2
Safety distance = 5m.
Vehicle profiles at time t=0
Comparative results (case 1)
• Total TTM– ASMC = 14.12 sec– HoL1 = 15.011sec– HoL2 = 14.12 sec
• Here HoL2 performs better than HoL1.
Graphs (case 1)
ASMC HoL2
C++ Simulation experiments (case 2)
Vehicle parameters :-Velocity bounds = [0,27] m/s
Acceleration bounds = [-4,4] m/s2
Safety distance = 5m
Vehicle profiles at time t=0
Comparative results (case 2)
• Total TTM– ASMC = 17.63 sec– HoL1 = 17.84 sec – HoL2 = 18.65 sec
• Here HoL1 performs better than HoL2
Graphs (case 2)
ASMC HoL1
Conclusions
Comparative study of Automatic merge
control algorithms.
Few approaches are proposed which
ensure safety and high traffic
throughput.
The HoL1 approach is observed to
perform better than HoL2 in certain
scenario and vice-versa.
VIDEO
Future work
To study the performance of the AMC algorithms on continuous streams of vehicles.
To perform experiments on robotic vehicular platforms.
To design and implement a decentralized controller for decision making i.e. to allow vehicles to take decision.
To study vehicle-to-vehicle communication aspects.
Post-merging safety and stability.
References[1] Gurulingesh G., Neera Sharma, K. Ramamritham and Sachitanand M. Efficient
Real-Time Support for Automotive Application : A Case Study. In Proceedings of the
RTCSA 2006, Sydney, Australia, Aug 2006.
[2] Xiao-Yun Lu. and Hedrick K.J. Longitudinal control algorithm for automated
vehicle merging. In Proceedings of IEEE Conference on Decision and Control,
volume 1, pages 450-455,2000.
[3] Hossein Jula, Elias B. Kosmatopoulos, and Petros A. Ioannou. Collision
Avoidance Analysis for Lane Changing and Merging. In IEEE Transactions on
Vehicular Technology, volume 49, pages 2295-2308, Nov 2000.
[4] A. Uno, T. Sakaguchi, and S. Tsugawa. A merging control algorithm based on
inter-vehicle communication. In Proceedings of IEEE/IEEJ/JSAI International
Conference on Intelligent Transportation Systems 1999, pages 783-787, 1999.
[5] Steven E. Shladover Xiao-Yun Lu., Han-Shue Tan. and J. Karl Hedrick.
Implementation of longitudinal control algorithm for vehicle merging. In
Proceedings of AVEC 2000 5th International Symposium on Advanced Vehicle
Control, Ann Arbor, Michigan, Aug 2000.
[6] Gurulingesh G., J Bharadia and K. Ramamritham. Towards Intelligent Vehicles :
Automatic
Merge Control. In RTSS Workshop 2006, Brazil, Dec 2006.
[7] Vipul Shingde. Report on Automatic Merge Control, Indian Institute of
Technology, Bom-
bay. 2006.
Thank you
Prof. A A Diwan
Prof. Kavi Arya
Prof. Krithi Ramamritham
Gurulingesh R
Vipul Shingde
Sachitanand Malewar
Components of AMC System
Local Learning (e.g. GPS, roadside sensors, etc.)
Communication capability
Inter-Vehicle communication
Vehicle-Roadside communication
Sensors
Speed
accelerometer
Vehicle Profile
Controller Centralized (Intersection
manager)
Decentralized
Assumptions in AMC algorithms
Vehicles are treated as points on the lane.
Vehicles know their location from intersection region.
Intersection manager exists at the intersection region. It does all the computations and controls the merging.
Communication exists between vehicles and intersection manager.
Vehicles are initially separated by safety distance.
Vehicles’ control system is reliable i.e. vehicles follow the behavior as commanded by intersection manager.
Optimization formulation[6]
• Minimize • Subject to
– Precedence constraint where is TTM of jth vehicle of ith lane and
– Mutual exclusion constraint• Where S=safety distance
– Lower bound on TTM• is max. velocity
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