Abstract: Among other things, a world model is maintained of an environment of a vehicle. A hypothetical object in the environment that cannot be perceived by sensors of the vehicle is included in the world model.

Abstract: Among other things, a world model is maintained of an environment of a vehicle. A hypothetical object in the environment that cannot be perceived by sensors of the vehicle is included in the world model.

Abstract: Among other things, a world model is maintained of an environment of a vehicle. A hypothetical object in the environment that cannot be perceived by sensors of the vehicle is included in the world model.

Abstract: A computer-based method controls a dynamical system in an uncertain environment within a bounded probability of failure. The dynamical system has a state space and a control space. The method includes diffusing a risk constraint corresponding to the bounded probability of failure into a martingale that represents a level of risk tolerance associated with the dynamical system over time. The state space and the control space of the dynamical system are augmented with the martingale to create an augmented model with an augmented state space and an augmented control space. The method may include iteratively constructing one or more Markov Decision Processes (MDPs), with each iterative MDP represents an incrementally refined model of the dynamical system. The method further includes computing a first solution based on the augmented model or, if additional time was available, based on one of the MDP iterations.

Abstract: Among other things, an operation related to control of a vehicle is facilitated by actions that include the following. A finite set of candidate trajectories of the vehicle is generated that begin at a location of the vehicle as of a given time. The candidate trajectories are based on a state of the vehicle and on possible behaviors of the vehicle and of the environment as of the location of the vehicle and the given time. A putative optimal trajectory is selected from among the candidate trajectories based on costs associated with the candidate trajectories. The costs include costs associated with violations of rules of operation of the vehicle. The selected putative optimal trajectory is used to facilitate the operation related to control of the vehicle.

Abstract: Among other things, an operation related to control of a vehicle is facilitated by actions that include the following. A finite set of candidate trajectories of the vehicle is generated that begin at a location of the vehicle as of a given time. The candidate trajectories are based on a state of the vehicle and on possible behaviors of the vehicle and of the environment as of the location of the vehicle and the given time. A putative optimal trajectory is selected from among the candidate trajectories based on costs associated with the candidate trajectories. The costs include costs associated with violations of rules of operation of the vehicle. The selected putative optimal trajectory is used to facilitate the operation related to control of the vehicle.

Abstract: Among other things, an operation related to control of a vehicle is facilitated by actions that include the following. A finite set of candidate trajectories of the vehicle is generated that begin at a location of the vehicle as of a given time. The candidate trajectories are based on a state of the vehicle and on possible behaviors of the vehicle and of the environment as of the location of the vehicle and the given time. A putative optimal trajectory is selected from among the candidate trajectories based on costs associated with the candidate trajectories. The costs include costs associated with violations of rules of operation of the vehicle. The selected putative optimal trajectory is used to facilitate the operation related to control of the vehicle.

Abstract: A computer-based method controls a dynamical system in an uncertain environment within a bounded probability of failure. The dynamical system has a state space and a control space. The method includes diffusing a risk constraint corresponding to the bounded probability of failure into a martingale that represents a level of risk tolerance associated with the dynamical system over time. The state space and the control space of the dynamical system are augmented with the martingale to create an augmented model with an augmented state space and an augmented control space. The method may include iteratively constructing one or more Markov Decision Processes (MDPs), with each iterative MDP represents an incrementally refined model of the dynamical system. The method further includes computing a first solution based on the augmented model or, if additional time was available, based on one of the MDP iterations.

Abstract: Among other things, an operation related to control of a vehicle is facilitated by actions that include the following. A finite set of candidate trajectories of the vehicle is generated that begin at a location of the vehicle as of a given time. The candidate trajectories are based on a state of the vehicle and on possible behaviors of the vehicle and of the environment as of the location of the vehicle and the given time. A putative optimal trajectory is selected from among the candidate trajectories based on costs associated with the candidate trajectories. The costs include costs associated with violations of rules of operation of the vehicle. The selected putative optimal trajectory is used to facilitate the operation related to control of the vehicle.

Abstract: A distributed traffic signal control method is provided for a directed network comprising a plurality of junctions, each junction having a plurality of links connected thereto, the links comprising one or more upstream links and one or more downstream links, the method comprising: activating one of a plurality of phases of the junction for a predetermined time period which maximizes the directed network throughput based on current differential traffic backlogs between said one or more upstream links and said one or more downstream links, each phase providing a unique combination of traffic signals at the junction for guiding traffic from the upstream link(s) to the downstream link(s). There is also provided a corresponding traffic signal controller, a traffic control system comprising the traffic signal controller, and a computer readable medium having stored therein computer executable codes for instructing a computer processor to execute the distributed traffic signal control method.

Abstract: A distributed traffic signal control method is provided for a directed network comprising a plurality of junctions, each junction having a plurality of links connected thereto, the links comprising one or more upstream links and one or more downstream links, the method comprising: activating one of a plurality of phases of the junction for a predetermined time period which maximizes the directed network throughput based on current differential traffic backlogs between said one or more upstream links and said one or more downstream links, each phase providing a unique combination of traffic signals at the junction for guiding traffic from the upstream link(s) to the downstream link(s). There is also provided a corresponding traffic signal controller, a traffic control system comprising the traffic signal controller, and a computer readable medium having stored therein computer executable codes for instructing a computer processor to execute the distributed traffic signal control method.

Abstract: A computer-based method controls a dynamical system in an uncertain environment within a bounded probability of failure. The dynamical system has a state space and a control space. The method includes diffusing a risk constraint corresponding to the bounded probability of failure into a martingale that represents a level of risk tolerance associated with the dynamical system over time. The state space and the control space of the dynamical system are augmented with the martingale to create an augmented model with an augmented state space and an augmented control space. The method may include iteratively constructing one or more Markov Decision Processes (MDPs), with each iterative MDP represents an incrementally refined model of the dynamical system. The method further includes computing a first solution based on the augmented model or, if additional time was available, based on one of the MDP iterations.