Sze Zheng Yong

A Survey of Motion Planning and Control Techniques for Self-Driving Urban Vehicles

Self-driving vehicles are a maturing technology with the potential to reshape mobility by enhancing the safety, accessibility, efficiency, and convenience of automotive transportation. Safety-critical tasks that must be executed by a self-driving vehicle include planning of motions through a dynamic environment shared with other vehicles and pedestrians, and their robust executions via feedback control. The objective of this paper is to survey the current state of the art on planning and control algorithms with particular regard to the urban setting. A selection of proposed techniques is reviewed along with a discussion of their effectiveness. The surveyed approaches differ in the vehicle mobility model used, in assumptions on the structure of the environment, and in computational requirements. The side by side comparison presented in this survey helps to gain insight into the strengths and limitations of the reviewed approaches and assists with system level design choices.

A unified filter for simultaneous input and state estimation of linear discrete-time stochastic systems

In this paper, we present a unified optimal and exponentially stable filter for linear discrete-time stochastic systems that simultaneously estimates the states and unknown inputs in an unbiased minimum-variance sense, without making any assumptions on the direct feedthrough matrix. We also provide the connection between the stability of the estimator and a system property known as strong detectability, and discuss the global optimality of the proposed filter. Finally, an illustrative example is given to demonstrate the performance of the unified unbiased minimum-variance filter.

Resilient state estimation against switching attacks on stochastic cyber-physical systems

In this paper, we address the resilient state estimation problem for some relatively unexplored security issues for cyber-physical systems, namely switching attacks and the presence of stochastic process and measurement noise signals, in addition to attacks on actuator and sensor signals. We model the systems under attack as hidden mode stochastic switched linear systems with unknown inputs and propose the use of the multiple model inference algorithm developed in [1] to tackle these issues. We also furnish the algorithm with the lacking asymptotic analysis. Moreover, we characterize fundamental limitations to resilient estimation (e.g., upper bound on the number of tolerable attacks) and discuss the issue of attack detection under this framework. Simulation examples of switching attacks on benchmark and power systems show the efficacy of our approach to recover unbiased state estimates.

Simultaneous input and state estimation for linear discrete-time stochastic systems with direct feedthrough

In this paper, we present an optimal filter for linear discrete-time stochastic systems with direct feedthrough that simultaneously estimates the states and unknown inputs in an unbiased minimum-variance sense. We argue that the information about the unknown input can be obtained from the current time step as well as the previous one, making it possible to estimate the unknown input in different ways. We then propose one variation of the filter that uses the updated state estimate to compute the best linear unbiased estimate (BLUE) of the unknown input. The comparison of the new filter and the filters in existing literature is discussed in detail and tested in simulation examples.

Simultaneous input and state estimation with a delay

In this paper, we present recursive algorithms for linear discrete-time stochastic systems that simultaneously estimate the states and unknown inputs in an unbiased minimum-variance sense with a delay. By allowing potential delays in state estimation, the stricter assumptions in a previous work [1] can be relaxed. Moreover, we show that a system property known as strong detectability plays a key role in the existence and stability of the asymptotic estimator with a delay we propose.

Generalized innovation and inference algorithms for hidden mode switched linear stochastic systems with unknown inputs

In this paper, we propose inference algorithms for simultaneously estimating the mode, input and state of hidden mode switched linear stochastic systems with unknown inputs. First, we define the generalized innovation for the recently proposed optimal filter for simultaneous input and state estimation [1] and show that the sequence is a Gaussian white noise. Then, we utilize this whiteness property of the generalized innovation, which reflects the estimation quality to form the likelihood function of the system model. Consequently, we employ the multiple model (MM) approach based on the likelihood function for inferring the hidden mode of switched linear stochastic systems. Algorithms for both static and dynamic MM estimation are presented and compared using a simulation example of vehicles at an intersection with switching driver intentions.

Hidden mode tracking control for a class of hybrid systems

In this paper, we consider the tracking control problem for a class of hidden mode hybrid systems in which the mode is not available for control. The time-varying reference trajectories are given by functions that may exhibit jumps. We tackle this problem by designing a sliding mode adaptive controller for the hybrid system to track well-posed time-varying reference trajectories that may exhibit jumps, using well-established tools for stabilization of hybrid systems. The approach is illustrated with examples.

Robust and resilient estimation for Cyber-Physical Systems under adversarial attacks

In this paper, we propose a novel state estimation algorithm that is resilient to sparse data injection attacks and robust to additive and multiplicative modeling errors. By leveraging principles of robust optimization, we construct uncertainty sets that lead to tractable optimization solutions. As a corollary, we obtain a novel robust filtering algorithm when there are no attacks, which can be viewed as a “frequentist” robust estimator as no known priors are assumed. We also describe the use of cross-validation to determine the hyperparameters of our estimator. The effectiveness of our estimator is demonstrated in simulations of an IEEE 14-bus electric power system.

Anytime computation algorithms for stochastically parametric approach-evasion differential games

We consider an approach-evasion differential game where the inputs of one of the players are upper bounded by a random variable. The game enjoys the order preserving property where a larger relaxation of the random variable induces a smaller value function. Two numerical computation algorithms are proposed to asymptotically recover the expected value function. The performance of the proposed algorithms is compared via a stochastically parametric homicidal chauffeur game. The algorithms are also applied to the scenario of merging lanes in urban transportation.

Simultaneous Input and State Estimation for Linear Time-Varying Continuous-Time Stochastic Systems

In this technical note, we consider the problem of optimal filtering for linear time-varying continuous-time stochastic systems with unknown inputs. We first show that the unknown inputs cannot be estimated without additional assumptions. Then, we discuss some conditions under which meaningful estimation is possible and propose an optimal filter that simultaneously estimates the states and unknown inputs in an unbiased minimum-variance sense. Conditions for uniform asymptotic stability, and the existence of a steady-state solution, as well as the convergence rate of the state and input estimate biases are given. Moreover, we show that a principle of separation of estimation and control holds and that the unknown inputs may be rejected. A nonlinear vehicle reentry example is given to illustrate that our filter is applicable even when some strong assumptions do not hold.