1. 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

2. 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)
University of California, Berkeley

3. 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

4. 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

5. Automated Platoons on I-15
University of California, Berkeley

6. 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
Join
Speed,
vehicle
following
Lane
Change
Platoon
Following
Split
Exit
University of California, Berkeley

7. 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
i-1
i-2 j
University of California, Berkeley

8. 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
University of California, Berkeley

9. 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, CDC98
University of California, Berkeley

10. 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

11. 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 intra-platoon 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

12. 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

13. 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

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

15. 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
University of California, Berkeley