from automated to manual -modeling control transitions ...€¦ · infrastructure-assisted driving...
TRANSCRIPT
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This project has received funding from the European Union’s Horizon 2020 research and innovation programme under grant agreement No 723390
From Automated to Manual - Modeling Control Transitions with SUMO
Leonhard Lücken, Evangelos Mintsis, Kallirroi Porfyri, Robert Alms,
Yun-Pang Flötteröd, Dimitris [email protected]
SUMO User Conference | May 2019
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TransAID - Transition Areas forInfrastructure-Assisted Driving
05/2019 | TransAID | SUMO User Conference 2
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Outline
●A Model for Automated Vehicles
●Transitions of Control and a Model
for human driving
●Traffic Management in
Transition Areas – Two Use Cases
05/2019 | TransAID | SUMO User Conference 3
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Models for automated vehicles
● ACC Car-Following Model [Milanés et al., 2014]
05/2019 | TransAID | SUMO User Conference 4
i. Speed control mode: is
designed to maintain the by
the driver chosen desired
speed,
ii. Gap control mode: aims to
maintain a constant time
gap between the controlled
vehicle and its predecessor,
iii. Gap-closing control
mode: enables the smooth
transition from speed
control mode to gap control
mode,
iv. Collision avoidance
mode: prevents rear-end
collisions.
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Parametrized Lane Change Model
05/2019 | TransAID | SUMO User Conference 5
i. Variance based sensitivity analysis
→ Influential lane change calibration parameters
ii. SUMO lane change output
vs HMETC lane change data
→ Reconciliation
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ToC / MRM Model
05/2019 | TransAID | SUMO User Conference 6
error-free„reducedperformance“
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Imperfect Driving
05/2019 | TransAID | SUMO User Conference 7
General CF Model:
Perceived quantities:
Erroneous CF Model:
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05/2019 | TransAID | SUMO User Conference 8
ToC / MRM Model
• https://sumo.dlr.de/wiki/Car-Following-Models/ACC
• https://sumo.dlr.de/wiki/ToC_Device
• https://sumo.dlr.de/wiki/Driver_State
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Traffic management in Transition AreasScenario 1
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TOR area
RSU
Merge area
Service „Path advice“
Headwayadvices
Path provision,Lane advices
automated, connected
manual
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Traffic management in Transition AreasScenario 1
05/2019 | TransAID | SUMO User Conference 10
Color ∼ ToC state
Color ∼ speed
MRM Post-ToC
manual automated
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Traffic management in Transition AreasScenario 1
Results● 1h random vehicle flow
(LoS C ~ 1155 veh/h)
● Fleet mixes (MV-AV):mix 1: 70-30mix 2: 50-50mix 3: 20-80
05/2019 | TransAID | SUMO User Conference 11
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0134 2
Traffic management in Transition AreasScenario 2
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control area
RSU
Service „TOR distribution“
No-AD info,TOR advices
automated, connected
manual
RSU
TakeoveraccumulationTakeover
distribution
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Traffic management in Transition AreasScenario 2
05/2019 | TransAID | SUMO User Conference 13
https://sumo.dlr.de/wiki/TraCI/Change_Vehicle_Statehttps://sumo.dlr.de/wiki/TraCI/Change_PoI_Statehttps://sumo.dlr.de/wiki/TraCI/Change_Polygon_State
Highlighting& polygondynamics
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Traffic management in Transition AreasScenario 2
Results● 1h random vehicle flow
(LoS C ~ 3234 veh/h)
● Fleet mixes (MV-AV):mix 1: 70-30mix 2: 50-50mix 3: 20-80
05/2019 | TransAID | SUMO User Conference 14
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Traffic management in Transition AreasScenario 2
Results
no-AD zone
Without traffic management With traffic management
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Summary
● Models:– New models for automated vehicles
(CFModels ACC + CACC)
– New model for simulation of control transitions
– Driver State model
● Assessment of TM procedures:– Safety improvements for smoother flows at lane drops
– Reducing perturbances by distribution of ToCs
● Upcoming:– Realistic simulation of communications
– Combination of TransAID Services
– Real world feasibility assessment
05/2019 | TransAID | SUMO User Conference 16
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Thank you!
05/2019 | TransAID | SUMO User Conference 17
See also:● Mintsis et al. 2018,
TransAID Deliverable 3.1● Maerivoet et al. 2018,
TransAID Deliverable 4.2
www.transaid.eu
Funding:
EU H2020, GNo 723390
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Imperfect Driving 1
05/2019 | TransAID | SUMO User Conference 18
General CF Model:
Perceived quantities:
Erroneous CF Model:
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Imperfect Driving 2
05/2019 | TransAID | SUMO User Conference 19
Perception errors:
Erroneous CF Model:
Error base process:
Base process coefficients:
= „awareness“
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Imperfect Driving 3
05/2019 | TransAID | SUMO User Conference 20
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