AutonoVi: Autonomous Vehicle Planning with Dynamic Maneuvers and Traffic Constraints

We present AutonoVi:, a novel algorithm for autonomous vehicle navigation that supports dynamic maneuvers and satisfies traffic constraints and norms. Our approach is based on optimization-based maneuver planning that supports dynamic lane-changes, swerving, and braking in all traffic scenarios and guides the vehicle to its goal position. We take into account various traffic constraints, including collision avoidance with other vehicles, pedestrians, and cyclists using control velocity obstacles. We use a data-driven approach to model the vehicle dynamics for control and collision avoidance. Furthermore, our trajectory computation algorithm takes into account traffic rules and behaviors, such as stopping at intersections and stoplights, based on an arc-spline representation. We have evaluated our algorithm in a simulated environment and tested its interactive performance in urban and highway driving scenarios with tens of vehicles, pedestrians, and cyclists. These scenarios include jaywalking pedestrians, sudden stops from high speeds, safely passing cyclists, a vehicle suddenly swerving into the roadway, and high-density traffic where the vehicle must change lanes to progress more effectively.Comment: 9 pages, 6 figure

Proximinality and co-proximinality in metric linear spaces

As a counterpart to best approximation, the concept of best coapproximation was introduced in normed linear spaces by C. Franchetti and M. Furi in 1972. Subsequently, this study was taken up by many researchers. In this paper, we discuss some results on the existence and uniqueness of best approximation and best coapproximation when the underlying spaces are metric linear spaces

On strong proximinality in normed linear spaces

The paper deals with strong proximinality in normed linear spaces. It is proved that in  a compactly locally uniformly rotund Banach space, proximinality, strong proximinality, weak approximative compactness and  approximative compactness are all equivalent for closed convex sets. How strong proximinality can be transmitted to and from quotient spaces has also been discussed

SPA: Verbal Interactions between Agents and Avatars in Shared Virtual Environments using Propositional Planning

We present a novel approach for generating plausible verbal interactions between virtual human-like agents and user avatars in shared virtual environments. Sense-Plan-Ask, or SPA, extends prior work in propositional planning and natural language processing to enable agents to plan with uncertain information, and leverage question and answer dialogue with other agents and avatars to obtain the needed information and complete their goals. The agents are additionally able to respond to questions from the avatars and other agents using natural-language enabling real-time multi-agent multi-avatar communication environments. Our algorithm can simulate tens of virtual agents at interactive rates interacting, moving, communicating, planning, and replanning. We find that our algorithm creates a small runtime cost and enables agents to complete their goals more effectively than agents without the ability to leverage natural-language communication. We demonstrate quantitative results on a set of simulated benchmarks and detail the results of a preliminary user-study conducted to evaluate the plausibility of the virtual interactions generated by SPA. Overall, we find that participants prefer SPA to prior techniques in 84\% of responses including significant benefits in terms of the plausibility of natural-language interactions and the positive impact of those interactions

Generating Pedestrian Trajectories Consistent with the Fundamental Diagram Based on Physiological and Psychological Factors

Pedestrian crowds often have been modeled as many-particle system including microscopic multi-agent simulators. One of the key challenges is to unearth governing principles that can model pedestrian movement, and use them to reproduce paths and behaviors that are frequently observed in human crowds. To that effect, we present a novel crowd simulation algorithm that generates pedestrian trajectories that exhibit the speed-density relationships expressed by the Fundamental Diagram. Our approach is based on biomechanical principles and psychological factors. The overall formulation results in better utilization of free space by the pedestrians and can be easily combined with well-known multi-agent simulation techniques with little computational overhead. We are able to generate human-like dense crowd behaviors in large indoor and outdoor environments and validate the results with captured real-world crowd trajectories

Simulating Plausible Movement-based Interactions between Agents and Avatars using Biomechanical Principles and Psychological Factors

Many Virtual Reality-based applications enable the user to actively participate in the virtual environment (VE) by embodying a virtual avatar, and these applications rely on the presence of interactive virtual pedestrians, or agents, to enhance the plausibility of the simulation. A key challenge is to generate plausible movements, in terms of navigation paths and full-body animation, for each virtual agent as it interacts with the user's avatar and other agents. The overall effectiveness of the application depends on both, the realism of the agent's movements, and the faithful representation of the user's avatar. The primary goal of this dissertation is to present novel techniques to simulate plausible movements and behaviors for virtual agents, to synthesize personalized avatars that reflect the user's unique motion, and to generate plausible avatar-agent interactions in immersive VE's. First, we present a novel approach that generates trajectories for agents that reflect the speed and density relationship observed in human crowds. Our approach models the biomechanical relationship between stride length and walking speed, as well as psychological factors such as preference for personal space. Second, we propose a velocity computation algorithm that takes into account human motion constraints and generates plausible full body motion for the virtual agents and movement-based avatar-agent interactions. Third, we present an algorithm that simulates plausible gaze-based behaviors in avatar-agent interactions. Our approach relies on a Bayesian interpretation of the Theory of Mind concept to infer the user's intentions and generates an appropriate response for the virtual agents. Finally, we study the perception of motion rendered on virtual avatars and propose a novel data-driven approach to rapidly synthesize a photo-realistic virtual avatar that reflects the user's unique gait. We highlight the interactive performance of our algorithms in complex virtual environments with tens of virtual agents. We also conduct extensive user studies and show that the interactions generated using our proposed algorithms are perceived as more plausible, and that they have a greater impact on the user in immersive settings.Doctor of Philosoph

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