Risk-Aware Reasoning for Autonomous Vehicles

arXiv.org Artificial Intelligence

Risk-A ware Reasoning for Autonomous V ehicles Majid Khonji, Jorge Dias, and Lakmal Seneviratne Abstract -- A significant barrier to deploying autonomous vehicles (A Vs) on a massive scale is safety assurance. Several technical challenges arise due to the uncertain environment in which A Vs operate such as road and weather conditions, errors in perception and sensory data, and also model inaccuracy. In this paper, we propose a system architecture for risk-aware A Vs capable of reasoning about uncertainty and deliberately bounding the risk of collision below a given threshold. We discuss key challenges in the area, highlight recent research developments, and propose future research directions in three subsystems. First, a perception subsystem that detects objects within a scene while quantifying the uncertainty that arises from different sensing and communication modalities. Second, an intention recognition subsystem that predicts the driving-style and the intention of agent vehicles (and pedestrians). Third, a planning subsystem that takes into account the uncertainty, from perception and intention recognition subsystems, and propagates all the way to control policies that explicitly bound the risk of collision.


Approximate Inference in Probabilistic Graphical Models with Determinism

AAAI Conferences

In the proposed thesis, we study a special class of belief networks which contain both probabilistic and deterministic information. Deterministic information occurs as zero probabilities in the belief network. A majority of the work in the belief network community (see for example papers in conferences like UAI, AAAI, IJCAI and NIPS) addresses probabilistic inference tasks under the assumption that the underlying joint distribution represented by the belief network is strictly positive i.e. devoid of any determinism. The positivity assumption is problematic because (a) modeling many real-world problems such as genetic linkage analysis (Fishelson & Geiger 2003) requires that the inference method reason with both probabilistic and deterministic information and (b) inference is harder in presence of determinism or extreme probabilities (Dagum & Luby 1993). The purpose of the proposed thesis is to study both the representational and algorithmic issues involved in modeling deterministic information along with the usual probabilistic information in a belief network.


An Overview of Some Recent Developments in Bayesian Problem-Solving Techniques

AI Magazine

The last few years have seen a surge in interest in the use of techniques from Bayesian decision theory to address problems in AI. Decision theory provides a normative framework for representing and reasoning about decision problems under uncertainty. Within the context of this framework, researchers in uncertainty in the AI community have been developing computational techniques for building rational agents and representations suited to engineering their knowledge bases. The articles cover the topics of inference in Bayesian networks, decision-theoretic planning, and qualitative decision theory. Here, I provide a brief introduction to Bayesian networks and then cover applications of Bayesian problem-solving techniques, knowledge-based model construction and structured representations, and the learning of graphic probability models.


An Overview of Some Recent Developments in Bayesian Problem-Solving Techniques

AI Magazine

The last few years have seen a surge in interest in the use of techniques from Bayesian decision theory to address problems in AI. Decision theory provides a normative framework for representing and reasoning about decision problems under uncertainty. Within the context of this framework, researchers in uncertainty in the AI community have been developing computational techniques for building rational agents and representations suited to engineering their knowledge bases. This special issue reviews recent research in Bayesian problem-solving techniques. The articles cover the topics of inference in Bayesian networks, decision-theoretic planning, and qualitative decision theory. Here, I provide a brief introduction to Bayesian networks and then cover applications of Bayesian problem-solving techniques, knowledge-based model construction and structured representations, and the learning of graphic probability models.


Verification for Machine Learning, Autonomy, and Neural Networks Survey

arXiv.org Artificial Intelligence

This survey presents an overview of verification techniques for autonomous systems, with a focus on safety-critical autonomous cyber-physical systems (CPS) and subcomponents thereof. Autonomy in CPS is enabling by recent advances in artificial intelligence (AI) and machine learning (ML) through approaches such as deep neural networks (DNNs), embedded in so-called learning enabled components (LECs) that accomplish tasks from classification to control. Recently, the formal methods and formal verification community has developed methods to characterize behaviors in these LECs with eventual goals of formally verifying specifications for LECs, and this article presents a survey of many of these recent approaches.