Currently, autonomous vehicles are being evaluated by how well they interact with humans without evaluating how well humans interact with them. Since people are not going to unanimously switch over to using autonomous vehicles, attention must be given to how well these new vehicles signal intent to human drivers from the driver’s point of view. Ineffective communication will lead to unnecessary discomfort among drivers caused by an underlying uncertainty about what an autonomous vehicle is or isn’t about to do. Recent studies suggest that humans tend to fixate on areas of higher uncertainty so scenarios that have a higher number of vehicle fixations can be reasoned to be more uncertain. We provide a framework for measuring human uncertainty and use the framework to measure the effect of empathetic vs non-empathetic agents. We used a simulated driving environment to create recorded scenarios and manipulate the autonomous vehicle to include either an empathetic or non-empathetic agent. The driving interaction is composed of two vehicles approaching an uncontrolled intersection. These scenarios were played to twelve participants while their gaze was recorded to track what the participants were fixating on. The overall intent was to provide an analytical framework as a tool for evaluating autonomous driving features; and in this case, we choose to evaluate how effective it was for vehicles to have empathetic behaviors included in the autonomous vehicle decision making. A t-test analysis of the gaze indicated that empathy did not in fact reduce uncertainty although additional testing of this hypothesis will be needed due to the small sample size.

System and software verification is a vital component in the development and reliability of cyber-physical systems - especially in critical domains where the margin of error is minimal. In the case of autonomous driving systems (ADS), the vision perception subsystem is a necessity to ensure correct maneuvering of the environment and identification of objects. The challenge posed in perception systems involves verifying the accuracy and rigidity of detections. The use of Spatio-Temporal Perception Logic (STPL) enables the user to express requirements for the perception system to verify, validate, and ensure its behavior; however, a drawback to STPL involves its accessibility. It is limited to individuals with an expert or higher-level knowledge of temporal and spatial logics, and the formal-written requirements become quite verbose with more restrictions imposed. In this thesis, I propose a domain-specific language (DSL) catered to Spatio-Temporal Perception Logic to enable non-expert users the ability to capture requirements for perception subsystems while reducing the necessity to have an experienced background in said logic. The domain-specific language for the Spatio-Temporal Perception Logic is built upon the formal language with two abstractions. The main abstraction captures simple programming statements that are translated to a lower-level STPL expression accepted by the testing monitor. The STPL DSL provides a seamless interface to writing formal expressions while maintaining the power and expressiveness of STPL. These translated equivalent expressions are capable of directing a standard for perception systems to ensure the safety and reduce the risks involved in ill-formed detections.

The seamless integration of autonomous vehicles (AVs) into highly interactive and dynamic driving environments requires AVs to safely and effectively communicate with human drivers. Furthermore, the design of motion planning strategies that satisfy safety constraints inherit the challenges involved in implementing a safety-critical and dynamics-aware motion planning algorithm that produces feasible motion trajectories. Driven by the complexities of arriving at such a motion planner, this thesis leverages a motion planning toolkit that utilizes spline parameterization to compute the optimal motion trajectory within a dynamic environment. Our approach is comprised of techniques originating from optimal control, vehicle dynamics, and spline interpolation. To ensure dynamic feasibility of the computed trajectories, we formulate the optimal control problem in relation to the intrinsic state constraints derived from the bicycle state space model. In addition, we apply input constraints to bound the rate of change of the steering angle and acceleration provided to the system. To produce collision-averse trajectories, we enforce extrinsic state constraints extracted from the static and dynamic obstacles in the circumambient environment. We proceed to exploit the mathematical properties of B-splines, such as the Convex Hull Property, and the piecewise composition of polynomial functions. Second, we focus on constructing a highly interactive environment in which the con- figured optimal control problem is deployed. Vehicle interactions are categorized into two distinct cases: Case 1 is representative of a single-agent interaction, whereas Case 2 is representative of a multi-agent interaction. The computed motion trajectories per each case are displayed in simulation.