Swaroop Darbha

Intelligent cruise control systems and traffic flow stability

In analogy to the flow of fluids, it is expected that the aggregate density and the velocity of vehicles in a section of a freeway adequately describe the traffic flow dynamics. The conservation of mass equation together with the aggregation of the vehicle following dynamics of controlled vehicles describes the evolution of the traffic density and the aggregate speed of a traffic flow. There are two kinds of stability associated with traffic flow problems - string stability (or car-following stability) and traffic flow stability. We make a clear distinction between traffic flow stability and string stability, and such a dis- tinction has not been recognized in the literature, thus far. String stability is stability with respect to intervehicular spacing; intuitively, it ensures the knowledge of the position and velocity of every vehicle in the traffic, within reasonable bounds of error, from the knowledge of the position and velocity of a vehicle in the traffic. String stability is analyzed without adding vehicles to or removing vehicles from the traffic. On the other hand, traffic flow stability deals with the evolution of traffic velocity and density in response to the ad- dition and/or removal of vehicles from the flow. Traffic flow stability can be guaranteed only if the velocity and density solutions of the coupled set of equa- tions is stable, i.e., only if stability with respect to automatic vehicle following and stability with respect to density evolution is guaranteed. Therefore, the ow stability and critical capacity of any section of a highway is dependent not only on the vehicle following control laws and the information used in their synthesis, but also on the spacing policy employed by the control system. Such a dependence has practical consequences in the choice of a spacing policy for adaptive cruise control laws and on the stability of the traffic ow consisting of vehicles equipped with adaptive cruise control features on the existing and future highways. This critical dependence is the subject of investigation in this paper. This problem is analyzed in two steps: The first step is to understand the effect of spacing policy employed by the Intelligent Cruise Control (ICC) systems on traffic flow stability. The second step is to understand how the dynamics of ICC system affects traffic flow stability. Using such an analysis, it is shown that cruise control systems that employ a constant time headway policy lead to unacceptable characteristics for the traffic flows. Key Words: Intelligent Cruise Control Systems, Traffic Flow Stability, String Stability, Advanced Vehicle Control Systems, Advanced Traffic Management Systems.

A Resource Allocation Algorithm for Multivehicle Systems With Nonholonomic Constraints

This paper is about the allocation of tours of m targets to n vehicles. The motion of the vehicles satisfies a nonholonomic constraint (i.e., the yaw rate of the vehicle is bounded). Each target is to be visited by one and only one vehicle. Given a set of targets and the yaw rate constraints on the vehicles, the problem addressed in this paper is 1) to assign each vehicle a sequence of targets to visit, and 2) to find a feasible path for each vehicle that passes through the assigned targets with a requirement that the vehicle returns to its initial position. The heading angle at each target location may not be specified. The objective function is to minimize the sum of the distances traveled by all vehicles. A constant factor approximation algorithm is presented for the above resource allocation problem for both the single and the multiple vehicle case. Note to Practitioners-The motivation for this paper stems from the need to develop resource allocation algorithms for unmanned aerial vehicles (UAVs). Small autonomous UAVs are seen as ideal platforms for many applications, such as searching for targets, mapping a given area, traffic surveillance, fire monitoring, etc. The main advantage of using these small autonomous vehicles is that they can be used in situations where a manned mission is dangerous or not possible. Resource allocation problems naturally arise in these applications where one would want to optimally assign a given set of vehicles to the tasks at hand. The feature that differentiates these resource allocation problems from similar problems previously studied in the literature is that there are constraints on the motion of the vehicle. This paper addresses the constraint that captures the inability of a fixed wing aircraft to turn at any arbitrary yaw rate. The basic problem addressed in this paper is as follows: Given n vehicles and m targets, find a path for each vehicle satisfying yaw rate contraints such that each target is visited exactly once by a vehicle and the total distance traveled by all vehicles is minimized. We assume that the targets are at least 2r apart, where r is the minimum turning radius of the vehicle. This is a reasonable assumption because the sensors on these vehicles can map or see an area whose width is at least 2r. We give an algorithm to solve this problem by combining ideas from the traveling salesman problem and the path planning literature. We also show how these algorithms perform in the worst-case scenario

Information flow and its relation to stability of the motion of vehicles in a rigid formation

It is known in the literature on automated highway systems that information flow can significantly affect the propagation of errors in spacing in a collection of vehicles. This paper investigates this issue further for a homogeneous collection of vehicles, where in the motion of each vehicle is modeled as a point mass. The structure of the controller employed by the vehicles is as follows: U/sub i/(s)=C(s)/spl Sigma/ /sub j/spl isin/si/(X/sub i/ - X/sub j/ - L/sub ij//s) where U/sub i/(s) is the (Laplace transformation of) control action for the i/sup th/ vehicle, L/sub ij/is the position of the i/sup th/ vehicle, L/sub ij/ is the desired distance between the i/sup th/ and the j/sup th/ vehicles in the collection, C(s) is the controller transfer function and S/sub i/ is the set of vehicles that the i/sup th/ vehicle can communicate with directly. This paper further assumes that the information flow is undirected, i.e., i/spl isin/S/sub j//spl harr/j/spl isin/S/sub i/, and the information flow graph is connected. We consider information flow in the collection, where each vehicle can communicate with a maximum of q(n) vehicles, such that q(n) may vary with the size n of the collection. We first show that C(s) cannot have any zeroes at the origin to ensure that relative spacing is maintained in response to a reference vehicle making a maneuver where its velocity experiences a steady state offset. We then show that if the control transfer function C(s) has one or more poles located at the origin of the complex plane, then the motion of the collection of vehicles will become unstable if the size of the collection is sufficiently large. These two results imply that C(0)/spl ne/0 and C(0) is well defined. We further show that if q(n)/sup 3//n/sup 2//spl rarr/0 as n /spl rarr//spl infin/ then there is a low frequency sinusoidal disturbance of at most unit amplitude acting on each vehicle such that the maximum errors in spacing response increase at least as O (/spl radic/(n/sup 2/)/q(n)/sup 3/). A consequence of the results presented in this paper is that the maximum of the error in spacing and velocity of any vehicle can be made insensitive to the size of the collection only if there is at least one vehicle in the collection that communicates with at least O(n/sup 2/3/) other vehicles in the collection.

Modeling the Pneumatic Subsystem of an S-cam Air Brake System

This paper deals with the development of a fault-free model of the pneumatic subsystem of an air brake system that is used in commercial vehicles. Our objective is to use this model in brake control and diagnostic applications. The development of a diagnostic system would be useful in automating enforcement inspections and also in monitoring the condition of the brake system in real-time. This paper presents a detailed description of the development of this model and of the experimental setup used to corroborate this model for various realistic test runs.

Today's Traveling Salesman Problem

Heterogeneous unmanned aerial vehicles (UAVs) are being developed for several civil and military applications. These vehicles can differ either in their motion constraints or sensing/attack capabilities. This article uses methods from operations research to address a fundamental routing problem involving heterogeneous UAVs. The approach is to transform the routing problem into a relatively better understood single, asymmetric, traveling salesman problem (ATSP) and use the algorithms available for the ATSP to address the routing problem. To test the effectiveness of the transformation, the well-known Lin-Kernighan-Helsgaun heuristic was applied to the transformed ATSP. Computational results on the transformed ATSP show that solutions whose costs are within 16% of the optimum can be obtained relatively fast [within 40 s of central processing unit (CPU)] for the routing problem involving ten heterogeneous UAVs and 40 targets.

Sampling-Based Path Planning for a Visual Reconnaissance Unmanned Air Vehicle

Nomenclature jAj = cardinality of a set A A , A, @A = interior, closure, and boundary of a set A, respectively C = cost of an aircraft reconnaissance tour , m d x;x0 = length of shortest aircraft path from state x to state x0, m nsamples = actual number of samples to build a roadmap nsamples = estimated number of samples to build a roadmap rmin = Dubins aircraft minimum turn radius R = s-dimensional Euclidean space S = circle parameterized by angle radians ranging from 0 to 2 SE(2) = special Euclidean group R S T = set fT 1; T 2; . . . ; T ng of n targets to be photographed by aircraft V T i = visibility region of ith target Va = Dubins aircraft airspeed X = aircraft state space x = aircraft state vector x; y = Dubins aircraft Earth-fixed Cartesian coordinates, m = parameter controls ratio of translational vs rotational density of roadmap = Dubins aircraft azimuth angle, rad 2 = set of all subsets of a set A

An approximation algorithm for a symmetric Generalized Multiple Depot, Multiple Travelling Salesman Problem

In this paper, we present an algorithm with an approximation factor of 2 for a Generalized, Multiple Depot, Multiple Travelling Salesman Problem (GMTSP) when the costs are symmetric and satisfy the triangle inequality. The algorithm requires finding a degree constrained minimum spanning tree which we compute using a Lagrangian relaxation.

Brief On the synthesis of controllers for continuous time LTI systems that achieve a non-negative impulse response

The problem of controlling the oscillatory response of a system has many practical applications. In this paper, I consider the problem of controlling the sign of the closed loop impulse response for a delay-free LTI system. If the impulse response of a stable system does not change sign, then its step response will neither undershoot nor overshoot. In this paper, I will show that such a synthesis is possible iff the open loop system does not have real, non-minimum phase zeros. In addition, I provide a stable compensator that achieves a stable, non-negative impulse response, if there exists one.

On the synthesis of controllers for a non-overshooting step response

In this paper, we show how a two-parameter compensator can always be designed for any Linear Time Invariant (LTI) plant, that does not have a zero at the origin, to render its step response non-overshooting.

A transformation for a Heterogeneous, Multiple Depot, Multiple Traveling Salesman Problem

Unmanned aerial vehicles (UAVs) are being increasingly used for surveillance missions in civil and military applications. These vehicles can be heterogeneous in the sense that they can differ either in their motion constraints or sensing/attack capabilities. Given a surveillance mission that require a group of heterogeneous UAVs to visit a set of targets, this paper addresses a resource allocation problem of finding the optimal sequence of targets for each vehicle such that 1) each target is visited at least once by some vehicle, and 2) the total cost travelled by all the vehicles is minimized. This problem can be posed as a Heterogeneous, Multiple Depot, Multiple Traveling Salesman Problem (HMDMTSP). This paper presents a transformation of a Heterogeneous, Multiple Depot, Multiple Traveling Salesman Problem (HMDMTSP) into a single, Asymmetric, Traveling Salesman Problem (ATSP). As a result, algorithms available for the single salesman problem can be used to solve the HMDMTSP. To show the effectiveness of the transformation, the well known Lin-Kernighan-Helsgaun heuristic was applied to the transformed ATSP. Computational results show that good quality solutions can be obtained for the HMDMTSP relatively fast.