How to Control Road Traffic Using AutomatedTruck Platoonshelper.ipam.ucla.edu/publications/avws3/avws3_16463.pdf · 2020. 11. 23. · Schittler, 2003; Scania, 2012 Fuel Total fuel

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How to Control Road Traffic Using Automated Truck Platoons

Karl H. JohanssonElectrical Engineering and Computer Science

KTH Royal Institute of TechnologySweden

Workshop III: Large Scale Autonomy: Connec9vity and Mobility NetworksIPAM Program: Mathema9cal Challenges and Opportuni9es for Autonomous Vehicles

Nov 16-20, 2020

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AcknowledgmentsAssad Alam, Ericsson Bart Besselink, U GroningenFarhad Farokhi, U MelbourneSebastian van de Hoef, HEREJeff Larson, Argonne NLKuo-Yun Liang, ScaniaEhsan Nekouei, City U HK

Matthieu BarreauMladen CicicDirk van DoorenYulong GaoFrank JiangAlexander JohanssonElis Stefansson

S. Amin (MIT), L. Jin (NYU), A. Ferrara (Pavia) and others

Valerio TurriJonas Mårtensson

Per Sahlholm, ScaniaAnton Selivanov, Sheffield UHåkan Terelius, Google

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The Problem

How to efficiently transport goods over a highway network?

Goal: Maximize automation and fuel-saving cooperationswith limited intervention in vehicle speed, route, and timing

Characteris:cs• 2 000 000 heavy long-haulage trucks in EU

- 400 000 in Germany• Large distributed control system with no real-Fme coordinaFon today• A few large and many small fleet owners with heterogeneous truck fleets

- 97% operate 20 or fewer trucks in US• Tight delivery deadlines and high expectaFons on reliability

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Why focus on fuel and automa1on?

Life cycle cost for European heavy-duty vehicle

Schittler, 2003; Scania, 2012

Fuel

Total fuel cost 80 k€/year/vehicle

Driver

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25 M EUR EU project on multi-brand platooning 2018-2021

B. Besselink et al., Cyber-physical control of road freight transport. Proceedings of IEEE, 104:5, 1128-1141, 2016.

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The Physics

Norrby (2014), Liang (2016)

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T2 PHighPLow LeadT3

Truck 1Truck 2Truck 3

Truck 1 Truck 2

Truck 3

Air d

rag

redu

ctio

n [%

]

Relative distance in platoon [m]

Air Drag Reduc-on in Truck Platooning

Wolf-Heinrich & Ahmed (1998), Bonnet & Fritz (2000), Scania CV AB (2011)

5-20% fuel reduction potential

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Vehicle System Architecture

Alam et al., 2014

Data from other vehicles

Pos from vehicle ahead

Own posiRon and velocity

EMS − Engine management systemBMS − Brake management systemGMS − Gear management system

CACC − CollaboraRve adapRve cruise controlACC − AdapRve cruise controlCC − Cruise control

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Platoon System Architecture

Alam et al., 2014

CACC − Collaborative adaptive cruise controlACC − Adaptive cruise controlCC − Cruise control

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Middleton & Braslavsky, 2010

How to Control Inter-vehicular Spacings?

• Limited sensing and inter-vehicle communica:on suggests distributed control strategy

• Important to a=enuate disturbances: string stability• Extensively studied problem in ideal environments

– E.g., Levine & Athans (1966), Peppard (1974), Ioannou & Chien (1993), Swaroop et al. (1994), Stankovic et al. (2000), Seiler et al. (2004), Naus et al. (2010)

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Experimental Setup

Alam, 2014

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Experimental Results

Alam, 2014

Platoon oscilla+ons

ChallengeHow to handle topography varia3ons?Which spacing policy to choose?

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Constant Time Gap Spacing PolicyFor the constant time gap policy it holds that

Control objec=ve:

Besselink & J, 2017

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Simula1ons with Platoon Coordinator and Look-ahead Road Grade Informa1on

Turri et al., 2015

Successful tracking of common platoon velocity reference

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5G Cellular Implementation of Platoon Coordinator

van Dooren et al., 2017

• Platoon coordinator generates common velocity reference:

• Can be computed in the cellular system • New handover scheme for moving control

computations between base stations

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Controller Code Handover Suppor0ng Vehicle Platooning

van Dooren et al., 2017, 2018

Control computations move within cellular networkunder guaranteed control performance

• Proposed new handover schemes for 5G• Support real-time control from edge cloud

5G test network @ KTH launch Dec 5, 2018

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Platoon Formation Merge and split vehicle

platoons on the flyPredictions on whether it is beneficial

for a vehicle to catch up another vehicle

Liang et al., 2016

Optimal speed profiles for platoon formation

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Platoon Forma?on

Liang et al., 2016; Cicic et al., 2017

Optimal speed profiles for platoon formation

FormationController

Traffic and Vehicle

Predictor

Feedback control of merging point based on real-time vehicle state and traffic information

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• Platoon formation of two trucks under various traffic conditions

• 600 test runs on E4 in Nov 2015• Traffic measurements from road

units together with onboard sensors

Platoon Formation Experiments

Liang et al., 2016

0 10 20 30 40 50 60 70 80 90 100 110 1200

500

1 000

1 500

2 000

2 500

Tra�c density [veh/km/lane]

Tra�cflow

[veh

/h/lan

e]

Fundamental diagram of traffic flow

830K measurements

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Traffic dynamics represented by extending the Daganzo (1994) cell transmission model (CTM) to handle moving boVlenecks

Platoon Formation Optimization

Cicic and J, 2019

Catch-up phase Platooning phase Split

v1, v2, vp 2 [vmin, vmax]<latexit sha1_base64="rxv4MLu+911wW3L2OFVRVxuoK+0=">AAACFnicbZC7TsMwFIYdriXcCowsFi0SQ1UlXYCtgoWxSIRWaqLIcZ3Wqu1EtlNRRXkLFl6FhQEQK2LjbXAvA7T80pE+/ecc2eePUkaVdpxva2V1bX1js7Rlb+/s7u2XDw7vVZJJTDycsER2IqQIo4J4mmpGOqkkiEeMtKPh9aTfHhGpaCLu9DglAUd9QWOKkTZWWK5Xq7avOGIMjkK3NgobplKfiu4ozH1ORVGbAnooArtaDcsVp+5MBZfBnUMFzNUKy19+L8EZJ0JjhpTquk6qgxxJTTEjhe1niqQID1GfdA0KxIkK8uldBTw1Tg/GiTQlNJy6vzdyxJUa88hMcqQHarE3Mf/rdTMdXwQ5FWmmicCzh+KMQZ3ASUiwRyXBmo0NICyp+SvEAyQR1iZK24TgLp68DF6jfll3bxuV5tU8jRI4BifgDLjgHDTBDWgBD2DwCJ7BK3iznqwX6936mI2uWPOdI/BH1ucP6UeeHw==</latexit><latexit sha1_base64="rxv4MLu+911wW3L2OFVRVxuoK+0=">AAACFnicbZC7TsMwFIYdriXcCowsFi0SQ1UlXYCtgoWxSIRWaqLIcZ3Wqu1EtlNRRXkLFl6FhQEQK2LjbXAvA7T80pE+/ecc2eePUkaVdpxva2V1bX1js7Rlb+/s7u2XDw7vVZJJTDycsER2IqQIo4J4mmpGOqkkiEeMtKPh9aTfHhGpaCLu9DglAUd9QWOKkTZWWK5Xq7avOGIMjkK3NgobplKfiu4ozH1ORVGbAnooArtaDcsVp+5MBZfBnUMFzNUKy19+L8EZJ0JjhpTquk6qgxxJTTEjhe1niqQID1GfdA0KxIkK8uldBTw1Tg/GiTQlNJy6vzdyxJUa88hMcqQHarE3Mf/rdTMdXwQ5FWmmicCzh+KMQZ3ASUiwRyXBmo0NICyp+SvEAyQR1iZK24TgLp68DF6jfll3bxuV5tU8jRI4BifgDLjgHDTBDWgBD2DwCJ7BK3iznqwX6936mI2uWPOdI/BH1ucP6UeeHw==</latexit><latexit sha1_base64="rxv4MLu+911wW3L2OFVRVxuoK+0=">AAACFnicbZC7TsMwFIYdriXcCowsFi0SQ1UlXYCtgoWxSIRWaqLIcZ3Wqu1EtlNRRXkLFl6FhQEQK2LjbXAvA7T80pE+/ecc2eePUkaVdpxva2V1bX1js7Rlb+/s7u2XDw7vVZJJTDycsER2IqQIo4J4mmpGOqkkiEeMtKPh9aTfHhGpaCLu9DglAUd9QWOKkTZWWK5Xq7avOGIMjkK3NgobplKfiu4ozH1ORVGbAnooArtaDcsVp+5MBZfBnUMFzNUKy19+L8EZJ0JjhpTquk6qgxxJTTEjhe1niqQID1GfdA0KxIkK8uldBTw1Tg/GiTQlNJy6vzdyxJUa88hMcqQHarE3Mf/rdTMdXwQ5FWmmicCzh+KMQZ3ASUiwRyXBmo0NICyp+SvEAyQR1iZK24TgLp68DF6jfll3bxuV5tU8jRI4BifgDLjgHDTBDWgBD2DwCJ7BK3iznqwX6936mI2uWPOdI/BH1ucP6UeeHw==</latexit><latexit sha1_base64="rxv4MLu+911wW3L2OFVRVxuoK+0=">AAACFnicbZC7TsMwFIYdriXcCowsFi0SQ1UlXYCtgoWxSIRWaqLIcZ3Wqu1EtlNRRXkLFl6FhQEQK2LjbXAvA7T80pE+/ecc2eePUkaVdpxva2V1bX1js7Rlb+/s7u2XDw7vVZJJTDycsER2IqQIo4J4mmpGOqkkiEeMtKPh9aTfHhGpaCLu9DglAUd9QWOKkTZWWK5Xq7avOGIMjkK3NgobplKfiu4ozH1ORVGbAnooArtaDcsVp+5MBZfBnUMFzNUKy19+L8EZJ0JjhpTquk6qgxxJTTEjhe1niqQID1GfdA0KxIkK8uldBTw1Tg/GiTQlNJy6vzdyxJUa88hMcqQHarE3Mf/rdTMdXwQ5FWmmicCzh+KMQZ3ASUiwRyXBmo0NICyp+SvEAyQR1iZK24TgLp68DF6jfll3bxuV5tU8jRI4BifgDLjgHDTBDWgBD2DwCJ7BK3iznqwX6936mI2uWPOdI/BH1ucP6UeeHw==</latexit>




Discretize Lighthill-Whitham-Richards PDE with truck platoon:

Mladen Cicic – [email protected]

Cell Transmission Model

ri(t+1)=ri(t)+TL�qi�1(t)�qi(t)

�

qi(t)=min�Di(t),Si+1(t)

�

Di(t)=min�Vri(t),Qmax

i�

Si(t)=min�Wi(Pi �ri(t)),Qmax

i�

• ri(t) – traffic density in cell i

• qi(t) – traffic flow from cell i to cell i+1• Di(t) – demand of cell i

• Si(t) – supply of cell i

• V – free flow speed

• Corresponds to discretization of the LWR model withNewell-Daganzo (triangular) flux function Q(r),

∂tr(x,t)+∂xQ(r(x,t)) = 0

Q(r) =

(Vr, r sW(P�r), r > s

[Daganzo 1994] , [Lighthill, Whitham 1955], [Lebacque 1995]

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• Higher fuel consumpFon during the catch-up phase• Lower fuel consumpFon during the platooning phase• Merge point depends on velociFes during the catch-up phase• Final split point is fixed to give desired average velocity

Platoon Forma/on Op/miza/on

Cicic and J, 2019

Catch-up phase Platooning phasev1, v2, vp 2 [vmin, vmax]

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Merge pointFinal split point

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sha1_base64="22+W6nxZhr7NvFxRvnICS2Iemdc=">AAACC3icdVDLSgMxFM3UVx1fVZdugq1QN2UyC9suhKIblxUcW+iUIZNm2tDMgyQjlKEf4MZfceNCxa0/4M6/MdMHqOiBwLnn3MvNPX7CmVSW9WkUVlbX1jeKm+bW9s7uXmn/4FbGqSDUITGPRdfHknIWUUcxxWk3ERSHPqcdf3yZ+507KiSLoxs1SWg/xMOIBYxgpSWvVK5UTJeMmIeqrsKpF56ez0p7WZqViu6yapZlIYRgTlD9zNKk2WzYqAFRbmmUwQJtr/ThDmKShjRShGMpe8hKVD/DQjHC6dR0U0kTTMZ4SHuaRjiksp/NjpnCE60MYBAL/SIFZ+r3iQyHUk5CX3eGWI3kby8X//J6qQoa/YxFSapoROaLgpRDFcM8GThgghLFJ5pgIpj+KyQjLDBROj9Th7C8FP5PHLvWrKFru9y6WKRRBEfgGFQBAnXQAlegDRxAwD14BM/gxXgwnoxX423eWjAWM4fgB4z3LyNkmLk=</latexit><latexit sha1_base64="22+W6nxZhr7NvFxRvnICS2Iemdc=">AAACC3icdVDLSgMxFM3UVx1fVZdugq1QN2UyC9suhKIblxUcW+iUIZNm2tDMgyQjlKEf4MZfceNCxa0/4M6/MdMHqOiBwLnn3MvNPX7CmVSW9WkUVlbX1jeKm+bW9s7uXmn/4FbGqSDUITGPRdfHknIWUUcxxWk3ERSHPqcdf3yZ+507KiSLoxs1SWg/xMOIBYxgpSWvVK5UTJeMmIeqrsKpF56ez0p7WZqViu6yapZlIYRgTlD9zNKk2WzYqAFRbmmUwQJtr/ThDmKShjRShGMpe8hKVD/DQjHC6dR0U0kTTMZ4SHuaRjiksp/NjpnCE60MYBAL/SIFZ+r3iQyHUk5CX3eGWI3kby8X//J6qQoa/YxFSapoROaLgpRDFcM8GThgghLFJ5pgIpj+KyQjLDBROj9Th7C8FP5PHLvWrKFru9y6WKRRBEfgGFQBAnXQAlegDRxAwD14BM/gxXgwnoxX423eWjAWM4fgB4z3LyNkmLk=</latexit> �1(⌧f ) = �2(⌧f ) = �f

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Merge point Final split point

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• Slowing down lead vehicle causes heavier traffic for follower vehicle• Fuel consumption reduced for proposed controller despite later merging

Numerical Example

Cicic and J, 2019

Catch-up phase Platooning phase Splitv1, v2, vp 2 [vmin, vmax]




Traffi

cde

nsity

Merge point without traffic

Merge point with traffic

Fuel-opCmal merge point

38

• Optimized platoon formation leads to 2-3% additional energy savings

• Considering the influence of trafficimproves energy savings

Energy Savings

Catch-up phase Platooning phase Splitv1, v2, vp 2 [vmin, vmax]




Relative energy savings with optimal control

Traffic congestion

Cicic and J, 2019

Optimal control, ignoring traffic

RecalculaCng opCmal control periodically, ignoring traffic condiCons

Recalculating optimal control periodically, incorporating traffic conditions

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Can truck platooning be used to improve traffic conditions?

Lin et al., 2018; Cicic and J, 2018

• Model truck platoons asboVlenecks moving in car traffic, cf., Lebacque et al. 1998; DelleMonache & GoaFn 2014

• Restrict the traffic flow into the congested areas using controlled truck platoons

DiscreFze the Lighthill-Whitham-Richards PDE model and include truck platoons:

41

Upstream-propagating wave of slowed or stationaryvehicles without an apparent bottleneck

Euler approach• Control of vehicle flows, e.g., variable speed limits

Lagrange approach• Control of individual vehicles or platoons,

e.g., connected automated vehicles

Stop-and-go wave dissipa/on

42

[Hegyi et al., 2008]

[Stern et al., 2018, Kreidieh et al., 2018, Čičić & J, 2018]

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Control truck platoon velocity to dissipate conges:onbased on traffic densi:es

Cicic and J, 2018

Traffic density with truck controlTraffic density without truck control

Truck platoon trajectory

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Truck platoon control reduces traffic conges0on

Cicic, Jin and J, 2019


Simulation results with an on- and off-ramp

Without control

+38.3% TTSWith control

+8.4% TTS

[Cicic, Jin, Johansson 2019]

| KTH | 13 of 14



Without control


+8.4% TTS


| KTH | 13 of 14

38% total travel Rme increasedue to traffic congesRon

Without truck platoon control With truck platoon control


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Truck platoon control reduces traffic conges0on

Cicic, Jin and J, 2019



Without control


+8.4% TTS


| KTH | 13 of 14



Without control


+8.4% TTS


| KTH | 13 of 14


Without truck platoon control With truck platoon control


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Stop-and-go wave dissipation based on state reconstruction

46

No control Perfect information control

• What if perfect traffic state informaDon is not available?• Use reconstructed traffic state based on adapDvely selected vehicle measurements

[Čičić et al., 2020]

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12

Traffic state reconstrucQonReconstruction-based control


No control Perfect informaAon control1. Probe vehicles detect congesCon

47


12


No control Perfect information control1. Probe vehicles detect congesCon

2. More probe vehicles are acCvated to beUer reconstruct the congesCon


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Stop-and-go wave dissipa4on based on state reconstruc4on

12


No control Perfect informaAon control1. Probe vehicles detect congestion

2. More probe vehicles are activated to better reconstruct the congestion

3. Actuator vehicles dissipate the stop-and-go wave


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Stop-and-go wave dissipa4on based on state reconstruc4on

12

No control Perfect information control

Traffic state reconstrucQonReconstrucQon-based control

1. Probe vehicles detect congestion2. More probe vehicles are activated to better reconstruct the congestion

3. Actuator vehicles dissipate the stop-and-go wave4. Next actuator vehicle dissipates the congestion caused by the first one


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Conclusions• Automated road freight transport

– Integrated platoon coordinator and cruise-controller– Platoon control over V2V and V2I cellular communication– Automated vehicle match-making and platoon formation

• Leads to significantly lower fuel and operation costs• Control automated platoons to

reduce traffic congestion• Platoons acting as probe vehicles (sensors) and

moving bottlenecks (actuators)

people.kth.se/~kallej

platoon coordinator

fleet layer roaddatabase

↵, vmax, vminv

vehicle controller 1

a⇤1(t) x1(t)

vs(·) x1(t)

vehicle controller 2

a⇤2(t) x2(t), d2(t)

vs(·) x2(t)

x1(·|t)

d2(t)

ENSEMBLE mulC-brand platooning H2020 project 2018-2021

Matthieu Barreau poster resentations at 11:50 (PT) on Learning-based Traffic State Reconstruction

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Documents

How to Control Road Traffic Using AutomatedTruck Platoonshelper.ipam.ucla.edu/publications/avws3/avws3_16463.pdf · 2020. 11. 23. · Schittler, 2003; Scania, 2012 Fuel Total fuel