Abstract: The present disclosure relates to telecommunications. In one of its aspects, the disclosure concerns a method in a Radio Access Network (RAN) node for updating a paging configuration for at least one User Equipment (UE) in a wireless communications system. The RAN node uses at least one radio beam for transmitting at least one paging message to said UE(s). The method comprises obtaining information regarding at least one paging condition. The at least one paging condition is associated with a current paging configuration for said at least one UE and comprises at least one of a false paging rate, latency, PDCCH load, and PDCCH and PDSCH imbalance. The method further comprises determining whether the obtained information regarding the at least one paging condition exceeds a respective associated threshold; and updating, based on the determination, the current paging configuration into an updated paging configuration for said at least one UE.

Abstract: A method, network node and wireless device (WD) for extended physical downlink control channel (PDCCH) monitoring are disclosed. According to one aspect, a method includes receiving a triggering signal, configured to trigger a search space expansion in the WD. The method also includes, when the triggering signal fulfills predetermined criteria, performing a search space expansion, the at least one predetermined criteria being based at least in part on at least one of physical downlink control channel, PDCCH, characteristics, physical uplink shared channel, PUSCH, characteristics and physical downlink shared channel, PDSCH, characteristics.

Abstract: Exemplary embodiments include methods for receiving one or more physical downlink control channels (PDCCHs) using a selectable number of available antennas and receive chains. Embodiments can include receiving a configuration associated with one or more PDCCHs, wherein the configuration includes, for each PDCCH, a search space comprising a plurality of PDCCH candidate. Embodiments can include selecting a first number of antennas and receive chains for PDCCH reception, wherein the first number comprises the minimum number of the available antennas and receive chains needed to meet one or more PDCCH performance metrics. Embodiments can include selecting, for PDCCH reception, either the first number of antennas and receive chains, or a greater second number of antennas and receive chains. Embodiments can include receiving the PDCCH using the selected number of antennas and receive chains, Other embodiments include UEs configured to perform operations corresponding to the exemplary methods.

Abstract: A method (1200) by a wireless device (110) includes receiving (1202), from a network node (160), a paging early indicator, PEI, configuration that includes an indication of a mapping of a PEI to a plurality of paging occasions, POs. The wireless device receives (1204) the PEI from the network node. Based on the mapping of the PEI to the plurality of POs, the wireless device monitors (1206) a shared channel during the plurality of POs.

Abstract: Embodiments include methods, performed by a network node in a wireless network, for managing energy consumption of user equipment (UEs) served by the network node. Such methods include transmitting, to a UE, a configuration including one or more time-domain resource allocations (TDRAs). The TDRAs include one or more first scheduling offsets and one or more second scheduling offsets between a scheduling message and a signal or channel scheduled via the scheduling message. A minimum value of the second scheduling offsets is greater than a minimum value of the first scheduling offsets. Such methods also include transmitting, to the UE, an indication of whether the UE should use the first scheduling offsets or the second scheduling offsets, and a first scheduling message that schedules a first signal or channel for the UE according to the indication. Embodiments also include complementary methods perform by UEs, as well as network nodes and UEs.

Abstract: A normalized driver performance comparison framework can include predicting a plurality of scenarios of a ground vehicle. A Monte Carlo simulation can be utilized to determine results using the predicted plurality of scenarios and one or more predictive models. Best and worst possible scenarios and associated efficiency for an observed scenario can be determined based on the Monte Carlo simulation results. The efficiency for the observed scenario can be normalized as a function of the best and worst efficiencies.

Abstract: A ground vehicle control system including a plurality of sensors and one or more predictive controllers. The sensors can be configured to detect environment information and vehicle operating information. The one or more predictive controllers can be configured to self-train for an energy consumption solution based on one or more control parameters including the environment information and the vehicle operating information.

Abstract: A computing device including a plurality of sensors, a system-on-module, a safety microcontroller and a plurality of communication interfaces communicatively coupling the system-on-module, the safety microcontroller and the plurality of sensors together. The system-on module can include an integrated interconnection of a plurality of different types of cores and one or more different types of memory. The system-on module can be configured to control operation and or performance of a system. The safety microcontroller can be configured to provide safety supervision of the system-on-module.

Abstract: Presented systems and methods facilitate efficient and effective performance of vehicle operations. In one embodiment, a system comprises a user interface, a processor, and a memory. The user interface is configured to convey information associated with operation of a vehicle to and from a user, wherein the information associated with the operation of the vehicle includes information associated with a performance objective. Information associated with the operation of the vehicle can include metric information that expresses a characteristic corresponding to the performance objective. The metric information can be associated with various aspects (e.g., current, future, etc.) of the vehicle operation. Processing by the processor can include comparative analysis of actual performance of the vehicle operation to target values associated with the vehicle operation. The user interface can include a performance indicator.

Abstract: Techniques for utilizing a Monte Carlo model to perform pre-training of a ground vehicle controller. A sampled distribution of actions and corresponding states can be utilized to train a reinforcement learning controller policy, learn an action-value function, or select a set of control parameters with a predetermined loss.

Abstract: Reinforcement learning based ground vehicle control techniques adapted to reduce energy consumption, braking, shifting, travel distance, travel time, and or the like. The reinforcement learning techniques can include training a reinforcement learning controller based on a simulated ground vehicle environment during a simulation mode, and then further training the reinforcement learning controller based on a ground vehicle environment during an operating mode of a ground vehicle.

Abstract: Ground vehicle control techniques adapted to reduce energy consumption, braking, shifting, travel distance, travel time, and or the like. The techniques can generate a target speed window and a target vehicle performance plan for controlling operation of a ground vehicle along a current and one or more upcoming segments of a roadway responsive to the dynamic driving environment.

Abstract: Presented systems and methods facilitate efficient and effective performance of vehicle operations. In one embodiment, a system comprises a user interface, a processor, and a memory. The user interface is configured to convey information associated with operation of a vehicle to and from a user, wherein the information associated with the operation of the vehicle includes information associated with a performance objective. Information associated with the operation of the vehicle can include metric information that expresses a characteristic corresponding to the performance objective. The metric information can be associated with various aspects (e.g., current, future, etc.) of the vehicle operation. Processing by the processor can include comparative analysis of actual performance of the vehicle operation to target values associated with the vehicle operation. The user interface can include a performance indicator.

Abstract: A normalized driver performance comparison framework can include predicting a plurality of scenarios of a ground vehicle. A Monte Carlo simulation can be utilized to determine results using the predicted plurality of scenarios and one or more predictive models. Best and worst possible scenarios and associated efficiency for an observed scenario can be determined based on the Monte Carlo simulation results. The efficiency for the observed scenario can be normalized as a function of the best and worst efficiencies.

Abstract: Ground vehicle control techniques adapted to reduce energy consumption, braking, shifting, travel distance, travel time, and or the like. The techniques can generate a target speed window and a target vehicle performance plan for controlling operation of a ground vehicle along a current and one or more upcoming segments of a roadway responsive to the dynamic driving environment.

Abstract: Reinforcement learning based ground vehicle control techniques adapted to reduce energy consumption, braking, shifting, travel distance, travel time, and or the like. The reinforcement learning techniques can include training a reinforcement learning controller based on a simulated ground vehicle environment during a simulation mode, and then further training the reinforcement learning controller based on a ground vehicle environment during an operating mode of a ground vehicle.

Abstract: Ground vehicle control techniques adapted to reduce energy consumption, braking, shifting, travel distance, travel time, and or the like. The techniques can generate a target speed window and a target vehicle performance plan for controlling operation of a ground vehicle along a current and one or more upcoming segments of a roadway responsive to the dynamic driving environment.

Abstract: Techniques for utilizing a Monte Carlo model to perform pre-training of a ground vehicle controller. A sampled distribution of actions and corresponding states can be utilized to train a reinforcement learning controller policy, learn an action-value function, or select a set of control parameters with a predetermined loss.

Abstract: A ground vehicle control system including a plurality of sensors and one or more predictive controllers. The sensors can be configured to detect environment information and vehicle operating information. The one or more predictive controllers can be configured to self-train for an energy consumption solution based on one or more control parameters including the environment information and the vehicle operating information.

Abstract: Systems and methods for discharging a high voltage bus of an electric vehicle are disclosed. In some instances, the discharge components coupled to the high voltage bus may be located in different remotely located portions of the electric vehicle to increase reliability of the high voltage bus discharge system.