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In this paper, we introduce cooperative autonomous driving algorithms for vehicular networks with nonlinear mobile robot dynamics in urban environments that take human safety into account and are capable of performing vehicle-to-vehicle (V2V) and vehicle-to-pedestrian (V2P) collision avoidance. We argue that “flocks” are multi-agent models of vehicular traffic on roads and propose novel autonomous driving architectures and algorithms for cyber-physical vehicles capable of performing autonomous driving tasks such as lane-driving, lane-changing, braking, passing, and making turns. Our proposed autonomous driving algorithms are inspired by Olfati-Saber's flocking theory. Though, there are notable differences between autonomous driving on urban roads and flocking behavior — flocks have a single desired destination whereas most drivers on road do not share the same destination. We refer to this collective behavior (driving) as “multi-objective flocking.” The self-driving vehicles in our framework turn out to be hybrid systems with a finite number of discrete states that are related to the driving modes of vehicles. Complex driving maneuvers can be performed using a sequence of mode switchings. We use near-identity nonlinear transformations to extend the application of particle-based autonomous driving algorithms to multi-robot networks with nonlinear dynamics. The derivation of the mode switching conditions that preserve safety is non-trivial and an important part of the design of autonomous driving algorithms. We present several examples of driving tasks that can be effectively performed using our proposed driving algorithms. [ABSTRACT FROM PUBLISHER]

We address the problem of tracking m targets using n mobile sensors for the case ofm ≫ n using a coupled estimation and control algorithm. We propose an information-driven flocking algorithm as a self-deployment algorithm for mobile sensor networks for multi-target tracking. The self-deployment algorithm is a modified form of Olfati-Saber's flocking algorithm [10] with n distinct γ-agents. The mobile sensors track their dedicated γ-agent that are located at the optimal points that maximize the information value of the collective sensed data by any sensor. A Fisher Information Matrix (FIM) based metric called the “information value function” is introduced to measure the quality of information sensed by mobile sensors with limited sensing range. The information-driven flocking algorithm attempts to simultaneously increase the sensor coverage and the information value while maintaining a minimum distance between the sensors and from the boundaries of the sensing region. Simulation results are presented for tracking 250 maneuvering targets in a rectangular region using 25 mobile sensors that are self-deployed using coupled information-driven flocking and information fusion. [ABSTRACT FROM PUBLISHER]