Intersecons of the Future: Using Fully Autonomous Vehicles

Prof. Peter Stone

Department of Computer Science The University of at Ausn

Department of Computer Science The University of Texas at Ausn Transportaon Infrastructure: Present and Future

• Today’s transportaon infrastructure is designed for human drivers. • In the future: Autonomous Traffic Management – Ulize the capacity of autonomous vehicles to improve traffic in transportaon systems. – Highly Efficient  Less fuel consumpon  Less emissions  Sustainable society aimautonomous management Autonomous Intersecon Management

• Dramacally reduce the traffic delay. • Reduce the overhead of fuel consumpon by approximately two thirds.

Kurt Dresner and Peter Stone. A Mulagent Approach to Autonomous Intersecon Management. JAIR 2008.

D. Fajardo, T.-C. Au, S. T. Waller, P. Stone, and D. Yang. Automated Intersecon Control: Performance of a Future Innovaon Versus Current Traffic Signal Control. In Transportaon Research Record : Journal of the Transportaon Research Board, 2011. Grid-Based Collision Detecon

Time 4

Time 3

Time 2

Time 1

Accept Reject Is the protocol safe? Safety Measures STOP

• Buffer • The protocol is fail-safe in the event of message dropping – If all autonomous vehicles follow the protocol, guarantee no collisions.

• When a crash occurs, sends STOP messages to all vehicles nearby. – Avoid most collisions. But some are unavoidable. Sharing the with Human Drivers

• Autonomous vehicles won’t displace manual-controlled vehicles in one day. • Some people enjoy driving. • FCFS-signal = First-Come, First-Served Policy + Traffic Signals Evaluang AIM with Real Autonomous vehicles

• Completely tesng AIM on Marvin – our autonomous vehicle real hardware requires a fleet of autonomous vehicles – Expensive and dangerous! • We implemented a mixed reality plaorm – Tesng a single real autonomous vehicle that interacts with many virtual (or simulated) vehicles. Mixed Reality Plaorm

Physical State of the Vehicle (GPS Location, Heading, Velocity, etc.)

Proxy vehicle

AIM Messages (Request, Confirm, etc.)

Sensing Information (Distance to the virtual vehicle in front, etc.) Mixed Reality in Acon Outline

• Introducon to AIM • Autonomous Traffic Management for Road Networks • Innovave Traffic Controls • Future Direcons Outline

• Introducon to AIM • Autonomous Traffic Management for Road Networks • Innovave Traffic Controls • Future Direcons Autonomous Traffic Management in Road Networks

Dynamic Route Planning

• Examined different navigaon policies by which autonomous vehicles can dynamically alter their planned paths • Braess’ paradox Braess’ Paradox

Phenomenon in which adding addional capacity to a network, when moving enes selfishly choose their routes, results in reduced overall performance.

Outline

• Introducon to AIM • Autonomous Traffic Management for Road Networks • Innovave Traffic Controls • Future Direcons Contraflow Reversal

• Increase the capacity of without increasing land use for transportaon. • Mainly use to control traffic during and emergency evacuaon Exisng Hardware for Lane Reversal

Signals of lane direcon Zipper machines

• Limitaons: – for certain hours and locaons only – must carefully plan ahead • Can we do beer? Dynamic Lane Reversal

• Yes, we can do beer. • Dynamic Lane Reversal – Safely and quickly change lane direcons at a much smaller mescale – Fast update of contraflow strategies for a road network • Benefits – adapt to the changing traffic condions Condions For Lane Reversal

• Under what condions would contraflow lane reversal would be beneficial? – A road – An intersecon – A road network Lane Reversal for a Road

λ1 ≤ C1

β0 β1

λ0 ≤ C0

• Capacity: C0 and C1

• Target traffic rates: β0 and β1

• Effecve traffic rates: λ0 = min(β0, C0) and λ1 = min(β1, C1)

• Throughput of the road: λ0 + λ1 Saturaon of a Road

λ1 ≤ C1

β0 β1

λ0 ≤ C0

• If β0 > c0, the eastbound are oversaturated.

• If β0 < c0, the eastbound lanes are undersaturated.

• If β0 = c0, the eastbound lanes are saturated. Necessary and Sufficient Condions for Lane Reversals for a Road

λ’1 ≤ C1 - CL

L β0 β1

λ’0 ≤ C0 + CL

• Criterion: λ0 + λ1 < λ’0 + λ’1 • Lane reversal is beneficial if and only if the eastbound

lanes are oversaturated by δ0 while the westbound lanes are undersaturated by δ1

– max(CL - δ1, 0) < δ0

Lane Reversal for an Intersecon controlled by Traffic Signals

• Number of Trials: 30 • 1 hour of simulaons in each trials Dynamic Lane Reversal (DLR)

400 350 300 250 DLR 200 348 No DLR 150 263 ±8.8 163 ±4.4 Traversal Time (Seconds) 100 ±5.2

Avg 113 50 ±4.1 0 Global Selecve

Experimental results averaged over 30 trials – each 1000 seconds. Mulcommodity Flow Problem

• A generalizaon of maximum flow problem • An NP-hard problem • Capacity constraint on each directed edges

C0 + C1 = C

s1 t1

s2 t2 Bi-Level Programming Formulaon

• Upper level: Allocaon of capacity to each direcon of all roads • Lower level: Solve the classic User Equilibrium model by Wardrop. • Genec Algorithms (GAs) – A gene represents the capacity of each direcon of roads. Maximum Flow vs. User Equilibrium

• The maximum flow problem has a unique soluon that is independent of vehicles’ behavior. • But drivers are self-interested – they do not cooperate to achieve the maximum flow • User equilibrium – the system behavior when each drivers minimizes their travel mes. Random Road Network

• Road network on a planar grid • Three types of roads: – (89%) – (10%) – Main road (1%) • Flows are generated by selecng source and sink randomly. Experimental Results with ILP

• 34 different networks – 10 × 10 intersecons • 10 hours of simulaons • 4 random flows per hour • Reconfiguraon period • Hourly reconfiguraon vs. stac configuraon – 72% increase in throughput Outline

• Introducon to AIM • Autonomous Traffic Management for Road Networks • Innovave Traffic Controls • Future Direcons Micro-tolling

• Congeson pricing at a very fine-grained level via aucon or dynamic road/intersecon pricing. • Incenvize cars to adjust their routes based on dynamically changing tolls. • Challenges: predict how the reroung strategy actually affects the equilibrium aer prices are changed. Conclusions and Future Work

• It is possible to make modern transportaon systems much more efficient. • Autonomous Driving • Mixed Reality Simulaon Plaorm • Autonomous Intersecon Management • Traffic management for road networks • Contraflow lane reversal • In the future – More efficient transportaon infrastructure to cope with increasing demand for transport Prof. Peter Stone Department of Computer Science The University of Texas at Ausn (Thanks to Kurt Dresner, Tsz-Chiu Au, Mahew Hausknecht, Travis Waller, FHWA, NSF)