Intelligent VehicleHighway Systems Shankar Sastry California PATH University

Intelligent Vehicle-Highway Systems Shankar Sastry California PATH University of California, Berkeley (Joint work with Datta Godbole, John Lygeros, Raja Sengupta & Shankar Sastry)

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 Systems) Systems

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

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)

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 • Flow optimization Entry Join Link Coordination Regulation • Dynamic routing • Maneuver selection • inter-vehicle comm Lane Change Speed, vehicle following • Lane keeping • Vehicle following University of California, Berkeley Platoon Following Split Exit

Vehicle Following & Lane Changing i i-1 i-2 j · 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

Game Theoretic Formulation · Requirements – Safety (no collision) – Passenger Comfort – Efficiency • trajectory tracking (depends on the maneuver) · Safe controller (J 1): Solve a two-person zero-sum game – saddle solution (u 1*, d 1*) given by • Both vehicles i and i-1 applying maximum braking • Both collisions occur at T=0 and with maximum impact University of California, Berkeley

Safe Vehicle Following Controller · Partition the state space into safe & unsafe sets Design comfortable and efficient controllers in the interior • IEEE TVT 11/94 Safe set characterization also provides sufficient conditions for lane change • CDC 97, CDC 98 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

AHS Performance Evaluation · 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 intraplatoon collisions requiring larger separation between two platoons · References – Carbaugh, Godbole, Sengupta, Transportation Research-C, 98 – Godbole, Lygeros, Transportation Research-C, 99 University of California, Berkeley

Highway Capacity Estimate (Single-Lane) N=Platoon size Queuing Analysis • Up to 20% capacity loss due to entry and exit • Up to 15% loss due to lane changes • Platoon Join/Split ? ? 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.

AHS Control Architecture Fault Mode i Network • Flow optimization Fault Mode j Link Network • Flow optimization • Dynamic routing Network • Flow optimization Link& efficient • Dynamic routing • Safe Coordination Control Switching Link • Dynamic routing • Inter-vehicle comm • Safe & efficient Coordination Control Switching • Multi-Objective Regulation • Inter-vehicle comm • Safe & efficient Control Design Coordination Control Switching • Multi-Objective • Inter-vehicle comm Regulation Control Design • Multi-Objective Regulation Control Design Analysis Methods and Tools System Performance Operating Scenario 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