Rajeev Verma

Development of a Scaled Vehicle With Longitudinal Dynamics of an HMMWV for an ITS Testbed

This paper applies Buckinghams pi theorem to the problem of building a scaled car whose longitudinal and power-train dynamics are similar to those of a full-size high-mobility multipurpose wheeled vehicle (HMMWV). The scaled vehicle uses hardware-in-the-loop (HIL) simulation to capture some of the scaled HMMWV dynamics physically, and simulates the remaining dynamics onboard in real time. This is performed with the ultimate goal of testing cooperative collision avoidance algorithms on a testbed comprising a number of these scaled vehicles. Both simulation and experimental results demonstrate the validity of this HIL-based scaling approach.

Safety Control of Hidden Mode Hybrid Systems

In this paper, we consider the safety control problem for hidden mode hybrid systems (HMHSs), which are a special class of hybrid automata in which the mode is not available for control. For these systems, safety control is a problem with imperfect state information. We tackle this problem by introducing the notion of nondeterministic discrete information state and by translating the problem to one with perfect state information. The perfect state information control problem is obtained by constructing a new hybrid automaton, whose discrete state is an estimate of the HMHS mode and is, as such, available for control. This problem is solved by computing the capture set and the least restrictive control map for the new hybrid automaton. Sufficient conditions for the termination of the algorithm that computes the capture set are provided. Finally, we show that the solved perfect state information control problem is equivalent to the original problem with imperfect state information under suitable assumptions. We illustrate the application of the proposed technique to a collision avoidance problem between an autonomous vehicle and a human driven vehicle at a traffic intersection.

Semiautonomous Multivehicle Safety

In this article, we have illustrated the application of a formal hybrid control approach to design semiautonomous multivehicle systems that are guaranteed to be safe. Our experimental results illustrate that, in a structured task, such as driving, simple human-decision models can be effectively learned and employed in a feedback control system that enforces a safety specification. They also highlight how the incorporation of these models in a safety control system makes the control actions required for safety less conservative. In fact, by virtue of the mode estimate, the current (mode-dependent) capture set to avoid guaranteeing safety is considerably smaller than the capture set to be avoided when the mode estimate is not available. This is essential for the practical applicability of cooperative active safety systems. In our data set, the flow entered the capture set only 3% times. These failures are mainly due to communication delays between the vehicles and the workstation. These delays, when significant, cause the calculated capture set to be different from the actual one and hence may cause to enforce control too late. These delays, in future work, should be formally accounted for in the models and in the safety control algorithm. More complex models of human decisions in the proximity of an intersection and the incorporation of additional details, such as weather conditions and road geometry, offer the potential for reducing the conservatism of safe control actions even further. Future work will also consider the extension to the case in which vehicles are not known to evolve on a fixed route. This case will be handled by keeping track of routes that are compatible with the position and speed of the vehicle and by progressively eliminating those that become incompatible. The models considered here are deterministic because most of the tools currently available to perform safety control have assumed deterministic models, wherein uncertainty is bounded. However, human decision models are more naturally captured by stochastic frameworks, in which uncertainty due to variability in both subjects and realizations of the same decision is probabilistic. As results in stochastic safety verification and design become available, it will be important to extend the proposed techniques of this article to safety control of stochastic hybrid automata in which the mode estimate is constructed probabilistically.

A separation principle for a class of hybrid automata on a partial order

We consider a parallel composition of two order preserving hybrid automata with imperfect state information. We show that the order preserving properties of the dynamics lead to a separation principle between state estimation and control under safety specifications. We provide a dynamic feedback algorithm that is guaranteed to terminate and whose complexity scales with the number of continuous variables.

Continuous control of hybrid automata with imperfect mode information assuming separation between state estimation and control

The safety control problem for hybrid automata with imperfect mode information and continuous control is addressed. When the controller does not have access to the mode of the system, available static feedback techniques cannot be applied. We propose a dynamic feedback strategy in which a mode estimator constructs the set of possible current system modes. A control map is designed that on the basis of the current mode estimates returns the set of all possible safe control inputs. This dynamic feedback map implicitly assumes separation between state estimation and control. Termination conditions are provided. The proposed control technique is applied to a semi-autonomous cooperative active safety system.

Control of hybrid automata with hidden modes: Translation to a perfect state information problem

In this paper, we consider the safety control problem for hybrid systems with hidden modes. In particular, we propose an approach to translate the control problem with imperfect mode information into an equivalent problem with perfect mode information. This approach is based on the notion of non-deterministic discrete information state as employed in the literature of games of imperfect information. We show that the safety control problems with imperfect information and perfect information are equivalent to each other under suitable detectability assumptions.

Control of Hidden Mode Hybrid Systems: Algorithm termination

We consider the problem of safety control in Hidden Mode Hybrid Systems (HMHS) that arises in the development of a semi-autonomous cooperative active safety system for collision avoidance at an intersection. We utilize the approach of constructing a new hybrid automaton whose discrete state is an estimate of the HMHS mode. A dynamic feedback map can then be designed that guarantees safety on the basis of the current mode estimate and the concept of the capture set. In this work, we relax the conditions for the termination of the algorithm that computes the capture set by constructing an abstraction of the new hybrid automaton. We present a relation to compute the capture set for the abstraction and show that this capture set is equal to the one for the new hybrid automaton.

Development and experimental validation of a semi-autonomous cooperative active safety system

In this paper, the problem of collision avoidance between two vehicles is considered, in which one vehicle is autonomous and the other one is human-driven. This problem arises in cooperative active safety systems at traffic intersections, mergings, and roundabouts, in which some vehicles are equipped with on-board communication and automatic control, while others are not capable of communicating and are human-driven. We model the human driving behavior through a hybrid automaton, whose current mode is determined by the drivers decisions, and solve the problem as a safety control problem for hybrid systems with imperfect state information. The experimental results demonstrate that our solution is substantially less conservative than solutions employing worst-case design.

LONGITUDINAL VEHICLE DYNAMICS SCALING AND IMPLEMENTATION ON A HIL SETUP

This paper presents the application of Buckingham’s π theorem to scale the powertrain of a High Mobility Multipurpose Wheeled Vehicle (HMMWV) by deriving non dimensional ratios called π parameters. A Hardware In the Loop (HIL) setup is constructed and the resulting longitudinal dynamics of the scaled vehicle are validated against those of a full scale vehicle model. This is performed with the ultimate goal of testing cooperative collision avoidance algorithms on a testbed comprising a number of these scaled vehicles. This paper is based on “Development of a scaled vehicle with Longitudinal dynamics of a HMMWV for ITS testbed”, by Verma, R., Domitilla Del Vecchio, and Hosam K. Fathy which appeared in IEEE/ASME Transactions on Mechatronics, February 2008 and is being reprinted with permission from IEEE.Copyright