University of California, Berkeley

Intelligent Vehicle-Highway Systems

Shankar Sastry

California PATH University of California, Berkeley

(Joint work with Datta Godbole, John Lygeros, Raja Sengupta & Shankar Sastry)

University of California, Berkeley

Intelligent Vehicle-Highway Systems (IVHS)

Partially or fully automate driving on the highways– can increase driving comfort and reduce stress– potential for increased safety

• 90% of all accidents are attributed to human error• Although many more hazards are successfully handled by humans.

– Automation can induce structured environment and tight control resulting in high capacity, less pollution & guaranteed travel times

Types of Automation – Driver Warning & Assistance (e.g., Blind Spot Warning)– Emergency Control (ABS,Daimler Chrysler schemes)– Control of Repetitive Tasks (Adaptive Cruise Control)– Complete Control (Automated Highway SystemsAutomated Highway Systems)

University of California, Berkeley

Control Problems in IVHS

Objectives– Increase safety & efficiency of the existing highway

infrastructure• objectives of the individual users and the system may not match

Characteristics– Control Design: Multiple Agents Compete for Scarce Resources

• Centralized control can yield optimal solutions but may be too complex and unreliable (danger of single point failure)

• Decentralized control increases reliability but may result in non-optimal or even unsafe solutions.

– Performance Evaluation• Performance metrics specified in terms of overall system whereas controllers

designed for individual vehicles• Evaluation in the uncertain environment of partial automation

University of California, Berkeley

Automated Highway System

Fully Automated Vehicles Operating on Dedicated Lanes– Involves control of individual vehicles as well as their

collective behavior Conflicting Objectives

– Safety & Capacity– Travel Time & Throughput (Individual vs System Optimal)

Definition of Safety– Ideally no collisions– Allowing low relative velocity collisions results in two

acceptable longitudinal vehicle following configurations• Following very close (platoon follower)• Following at sufficiently large distance (platoon leader)

University of California, Berkeley

Automated Platoons on I-15

University of California, Berkeley

Control of Automated Highway Systems

Design of vehicle controllers & performance estimation Two concepts

– platooning & individual vehicles

Network

Link

Coordination

Regulation •Lane keeping•Vehicle following

•Maneuver selection•inter-vehicle comm

•Dynamic routing

•Flow optimization Entry

Exit

LaneChange

PlatoonFollowing

Join

Split

Speed,vehiclefollowing

University of California, Berkeley

Vehicle Following & Lane Changing

Control actions: (vehicle i) -- braking, lane change Disturbances: (generated by neighboring vehicles) -- deceleration of the preceding vehicle

-- preceding vehicle colliding with the vehicle ahead of it

-- lane change resulting in a different preceding vehicles

-- appearance of an obstacle in front Operational conditions:

– state of vehicle i with respect to traffic

i

j

i-1 i-2

University of California, Berkeley

Game Theoretic Formulation

Requirements– Safety (no collision)

– Passenger Comfort

– Efficiency• trajectory tracking (depends on the maneuver)

Safe controller (J1): Solve a two-person zero-sum game

– saddle solution (u1*,d1*) given by• Both vehicles i and i-1 applying maximum braking• Both collisions occur at T=0 and with maximum impact

J x u d x t J Ct

10

03 1 1 0( , , ) inf ( );

J x u d u t J C mst

20

02 2

32 5( , , ) sup| ( )|; .

u U d D J x u d J x u d J x u d, , ( , , ) ( , , ) ( , , )* * * * * 10

1 10

1 1 10

1

University of California, Berkeley

Safe Vehicle Following Controller

Partition the state space into safe & unsafe sets0

min,304

02

01 ),,(: xxxxS

Design comfortable andefficient controllers inthe interior•IEEE TVT 11/94

Safe set characterizationalso provides sufficientconditions for lane change•CDC 97, CDC98

University of California, Berkeley

Automated Highway System Safety

Theorem 1: (Individual vehicle based AHS) – An individual vehicle based AHS can be designed to produce

no inter-vehicle collisions, – moreover disturbances attenuate along the vehicle string.

Theorem 2: (Platoon based AHS)– Assuming that platoon follower operation does not result in

any collisions even with a possible inter-platoon collision during join/split, a platoon based AHS can be safe under low relative velocity collision criterion.

References– Lygeros, Godbole, Sastry, IEEE TAC, April 1998– Godbole, Lygeros, IEEE TVT, Nov. 1994

University of California, Berkeley

Estimate maximum per lane capacity as a function of– vehicle braking rates, delays, types of coordination

Individual vehicles can increase highway capacity by a factor of two:– on-line estimation of braking capability

Platooning provides similar capacity with the possibility of low impact velocity collisions– Consider: emergency deceleration for obstacle avoidance

• differences in delays & braking rates give rise to multiple and severe intra-platoon collisions requiring larger separation between two platoons

References– Carbaugh, Godbole, Sengupta, Transportation Research-C, 98– Godbole, Lygeros, Transportation Research-C, 99

AHS Performance Evaluation

University of California, Berkeley

Highway Capacity Estimate (Single-Lane)

Queuing Analysis

•Up to 20% capacity loss due to entry and exit•Up to 15% loss due to lane changes•Platoon Join/Split ??

N=Platoon size

References•Transportation Research part-C: 1998, 1999

University of California, Berkeley

Fault Management

Faults induce switching of control strategies at multiple levels of hierarchy to maintain safety and minimize performance degradation

Design of fault management system– fault identification (distributed observation)– fault classification– fault handling

• minimal set of new maneuvers• fault localization• verified logical correctness of communication protocols

Need for probabilistic verification– worst-case design can not produce a safe system with faults

– given component reliability & Pd-fa characteristic of fault identification algorithms, compute probability of collisions.

University of California, Berkeley

AHS Control Architecture

Network

Link

Coordination

Regulation •Multi-ObjectiveControl Design

•Safe & efficientControl Switching•Inter-vehicle comm

•Dynamic routing

•Flow optimization

Network

Link

Coordination

Regulation

•Flow optimization

•Multi-ObjectiveControl Design

•Safe & efficientControl Switching•Inter-vehicle comm

•Dynamic routing

Network

Link

Coordination

Regulation •Multi-ObjectiveControl Design

•Safe & efficientControl Switching•Inter-vehicle comm

•Dynamic routing

•Flow optimization

Fault Mode i

Fault Mode j

AnalysisMethodsandTools

OperatingScenario

SystemPerformance

University of California, Berkeley

Deployment of AHS

Partial Automation yields progressive deployment path– Lack of structured environment– Lack of the knowledge of other driver’s intentions– Greedy driving policies– Human factors issues are highly pronounced

• false alarms, nuisance alarms, driver attentiveness, risk compensation, role confusion (Godbole et. al. TRB 98; James Kuchar at MIT)

Designing concepts for partial automation – ACC only roadway with infrastructure assisted entry

• (Godbole et. al. TRB 99)

Benefit Evaluation of partial automation systems– Hierarchical benefit evaluation methodology that integrates

analysis, simulation and experimentation results• adopted by NHTSA for crash avoidance systems analysis at VOLPE labs