Katherine Rose Driggs-Campbell

Improved driver modeling for human-in-the-loop vehicular control

In order to develop provably safe human-in-the-loop systems, accurate and precise models of human behavior must be developed. Driving is a good example of such a system because the driver has full control of the vehicle, and her likely actions are highly dependent on her mental state and the context of the current situation. This paper presents a testbed for collecting driver data that allows us to collect realistic data, while maintaining safety and control of the environmental surroundings. We extend previous work that focuses on set predictions consisting of trajectories observed from the nonlinear dynamics and behaviors of the human driven car, accounting for the driver mental state, the context or situation that the vehicle is in, and the surrounding environment in both highway and intersection scenarios. This allows us to predict driving behavior over long time horizons with extremely high accuracy. By using this realistic data and flexible algorithm, a precise and accurate driver model can be developed that is tailored to an individual and usable in semi-autonomous frameworks.

Identifying Modes of Intent from Driver Behaviors in Dynamic Environments

In light of growing attention of intelligent vehicle systems, we propose developing a driver model that uses a hybrid system formulation to capture the intent of the driver. This model hopes to capture human driving behavior in a way that can be utilized by semi-and fully autonomous systems in heterogeneous environments. We consider a discrete set of high level goals or intent modes, that is designed to encompass the decision making process of the human. A driver model is derived using a dataset of lane changes collected in a realistic driving simulator, in which the driver actively labels data to give us insight into her intent. By building the labeled dataset, we are able to utilize classification tools to build the driver model using features of based on her perception of the environment, and achieve high accuracy in identifying driver intent. Multiple algorithms are presented and compared on the dataset, and a comparison of the varying behaviors between drivers is drawn. Using this modeling methodology, we present a model that can be used to assess driver behaviors and to develop human-inspired safety metrics that can be utilized in intelligent vehicular systems.

Improving human-in-the-loop decision making in multi-mode driver assistance systems using hidden mode stochastic hybrid systems

Existing commercial driver assistance systems, including automatic braking systems and lane-keeping systems, may monitor the state of the vehicle or the environment to determine whether the systems should intervene. However, the state of the human driver is not typically included in the decision making process. In this paper, we propose to use hidden mode stochastic hybrid systems to model the interaction between the human driver and the vehicle. We show that by monitoring the human behavior as well as the vehicle state, we can infer the human state and enhance the quality of decision making in a driver assistance system. The resulting control policy is obtained by solving an optimal planning problem of the proposed hidden mode hybrid system. The policy can automatically balance the decision making about when to give warning to the driver and when to actually intervene in the control of the vehicle.

Integrating Intuitive Driver Models in Autonomous Planning for Interactive Maneuvers

Given the current capabilities of autonomous vehicles, one can easily imagine autonomous vehicles being released on the road in the near future. However, it can be assumed that this transition will not be instantaneous, suggesting that autonomous vehicles will have to be capable of driving in a mixed environment, with both humans and autonomous vehicles. To guarantee smooth integration and maintain the nuanced social interactions on the road, a shared mental model must be developed. This means that the behaviors of human-driven vehicles and their typical interactions in collaborative maneuvers must be modeled and understood in an accurate and precise manner. Then, by integrating such models into autonomous planning, we can develop control frameworks that mimic this shared understanding. We present a driver modeling framework that estimates an empirical reachable set to capture typical lane changing behaviors. This method can predict driver behaviors with up to 90% accuracy and cumulative errors less than 1 m. Leveraging this driver model in an optimization-based trajectory planning framework, we can generate trajectories that are similar to those performed by humans. By using this modeling and planning framework, we can improve understanding and integration of nuanced interactions to improve collaboration between humans and autonomy.

Towards trustworthy automation: User interfaces that convey internal and external awareness

Suppose we are given an autonomous vehicle that has limitations, meaning that it may need to transfer control back to the human driver to guarantee safety in certain situations. This paper presents work on designing a user interface to assist this hand off by considering the effects of the expression of internal and external awareness. Internal awareness is the concept of knowing whether or not the system is confident in its ability to handle the current situation. External awareness is the concept of being able to identify the limitations as the car is driving in terms of situational anomalies. We conduct a user study to examine what information should be presented to the driver, as well as the effects of expressing these levels of awareness on the drivers situational awareness and trust in the automation. We found that expressing uncertainty about the autonomous system (internal awareness) had an adverse effect on driver experience and performance. However, by effectively conveying the automations external awareness on the anomaly, improvements were found in the drivers situational awareness, increased trust in the system, and performance after the transfer of control.

User interface design and verification for semi-autonomous driving

This paper presents a project in its early stages of development, in which we propose a solution to the problem of human interaction with autonomous vehicles. We have devised a method for design of a user interface that displays sufficient and crucial information to the driver. Our contribution in this work is (i) identifying different modes of driving behavior, (ii) building an expectation model of a driver, and (iii) implementing an interface system.

Decisions for autonomous vehicles: integrating sensors, communication, and control

This paper details the work in progress to formalize methods and algorithms for autonomous decision making, focusing on the implementation of autonomous vehicles. Many different scenarios are to be considered while focusing on a heterogeneous environment of human driven, semi-autonomous, and fully autonomous vehicles. As this work is in its early stages of development, this paper summarizes the work that has been done in the areas of vehicle to vehicle communication with control applications and high-level decision making for autonomous vehicles. The proposed method to be implemented is also presented, which aims to guarantee feasibility, safety, and stability of autonomous systems.

Communicating intent on the road through human-inspired control schemes

Given the current capabilities of autonomous vehicles, one can easily imagine autonomy released on the road in the near future. However, it can be assumed that the transition will not be instantaneous, meaning they will have to be capable of driving well in a mixed environment, with both humans and other autonomous vehicles on the road. This leaves a number of concerns for autonomous vehicles in terms of dealing with human uncertainty and understanding of cooperation on the road. This work demonstrates the need for focusing on communication and collaboration between autonomy and human drivers. After analyzing how drivers perform cooperative maneuvers (e.g. lane changing), key cues were identified for conveying intent through nonverbal communication. It was found that human observers can predict lane changes with over two seconds in prior to the lane departure, without use of a turning signal. Building on this concept, an autonomous control scheme is proposed that aims to capture these subtle motions before executing a lane change. To compare the proposed human-inspired methods, three possible control schemes for autonomous vehicles are implemented for a validation study on human subjects to provide feedback on their experience. By properly conveying intent through nuanced trajectory planning, we show that drivers can predict the autonomous vehicles actions with 40% increase in prediction time when compared to traditional control methods, both as a passenger and while observing the autonomous vehicle.

Comparing datasets for generalizing models of driver intent in dynamic environments

In light of growing attention of intelligent vehicle systems, we have present an assessment of methods for driver models that predict driver behaviors. This work looks at varying datasets to see the affects on intent detection algorithms. The motivation is to understand and assess how data is mapped from datasets to discrete states or modes of intent. Using a model of a human drivers decision making process to estimate intent, we build techniques for analyzing and learning human behaviors to improve understanding. We derive models based off of human perception and interaction with the environment (e.g. other vehicles on the road), that is generalizable and flexible enough to detect intent across different drivers. The resulting detection scheme is able to determine driver intent with high accuracy across multiple drivers, relying on a large dataset consisting of lane changes under varying environmental constraints. By comparing different labeling methods, we assess the effectiveness of learned models under different class variations. This allows us to derive accurate and general models for detecting intent that rely on the subtle variations and behaviors that humans exhibit while driving.