Abstract: An example system for performing segmentation of data based on tensor inputs includes memory storing computer-executable instructions defining a learning network, where the learning network includes a plurality of sequential encoder down-sampling blocks. A processor is configured to execute the computer-executable instructions to receive a multi-dimensional input tensor including at least a first dimension, a second dimension and a plurality of channels. The processor is also configured to process the received multi-dimensional input tensor by passing the received multi-dimensional input tensor through the plurality of sequential encoder down-sampling blocks of the learning network, and to generate an output tensor in response to processing the received multi-dimensional input tensor. The output tensor includes at least one segmentation classification.

Abstract: Systems and methods for controlling a vehicle are described. A processor of a longitudinal planning system determines a state of the vehicle. The processor determines a state of a leader vehicle. The processor, based on the determined state of the vehicle and the determined state of the leader vehicle, determines a critical distance for the vehicle. The processor compares a distance between the vehicle and the leader vehicle with the critical distance. The processor, based on the comparison, determines whether the vehicle is too close to or too far from the leader vehicle. The processor, based on the determination, applies one or more of overshoot constraints, undershoot constraints, and critical constraints. After applying the one or more of overshoot constraints, undershoot constraints, and critical constraints, the processor determines a target acceleration for the vehicle. The processor controls the vehicle to track the target acceleration for the vehicle.

Abstract: An apparatus and a method for controlling a velocity of an autonomous driving vehicle is provided. The method includes steps of: obtaining information of an environment surrounding the vehicle when an obstacle is detected to be on a planning path of the vehicle; obtaining an initial reference velocity profile of the vehicle; determining a safety factor based on the initial reference velocity profile, the information of the environment and information of the vehicle, wherein the safety factor at least comprises a safety distance between the vehicle and the obstacle for the vehicle to follow the obstacle; determining an optimized reference velocity profile based on the information of the environment, the information of the vehicle and the safety factor; and performing the step of determining the safety factor by using the optimized reference velocity profile as the initial reference velocity profile and the step of determining the optimized reference velocity iteratively.

Abstract: Systems and methods for controlling a vehicle are described. A processor of a longitudinal planning system determines a state of the vehicle. The processor determines a state of a leader vehicle. The processor, based on the determined state of the vehicle and the determined state of the leader vehicle, determines a critical distance for the vehicle. The processor compares a distance between the vehicle and the leader vehicle with the critical distance. The processor, based on the comparison, determines whether the vehicle is too close to or too far from the leader vehicle. The processor, based on the determination, applies one or more of overshoot constraints, undershoot constraints, and critical constraints. After applying the one or more of overshoot constraints, undershoot constraints, and critical constraints, the processor determines a target acceleration for the vehicle. The processor controls the vehicle to track the target acceleration for the vehicle.

Abstract: Systems and methods for controlling a vehicle are described. A processor of a longitudinal planning system determines a state of the vehicle. The processor determines a state of a leader vehicle. The processor, based on the determined state of the vehicle and the determined state of the leader vehicle, determines a critical distance for the vehicle. The processor compares a distance between the vehicle and the leader vehicle with the critical distance. The processor, based on the comparison, determines whether the vehicle is too close to or too far from the leader vehicle. The processor, based on the determination, applies one or more of overshoot constraints, undershoot constraints, and critical constraints. After applying the one or more of overshoot constraints, undershoot constraints, and critical constraints, the processor determines a target acceleration for the vehicle. The processor controls the vehicle to track the target acceleration for the vehicle.

Abstract: An Internet of Things (IoT) technique for vehicular traffic management, including an IoT sensor to measure traffic data of vehicular traffic, a traffic analyzer to determine a traffic event based on the traffic data, and an IoT gateway to issue a response based on the traffic event.

Abstract: Methods and systems for autonomously steering a moving vehicle are disclosed. A processor determines a longitudinal velocity, a longitudinal acceleration, a lateral acceleration, and a yaw rate of the vehicle. The processor estimates, based on the longitudinal velocity, lateral acceleration, and yaw rate of the vehicle, a change in lateral velocity over time. The processor estimates, based on the change in the lateral velocity over time, the yaw rate, a distance between the front axle of the vehicle and a center of gravity of the vehicle, and a distance between the rear axle of the vehicle and the center of gravity of the vehicle, a lateral front velocity of the vehicle and a lateral rear velocity of the vehicle. Using calculations, a state estimation model for the vehicle is updated by the processor using a lateral acceleration bias. The updated state estimation model is used to autonomously steer the vehicle.

Abstract: An example system for performing segmentation of data based on tensor inputs includes memory storing computer-executable instructions defining a learning network, where the learning network includes a plurality of sequential encoder down-sampling blocks. A processor is configured to execute the computer-executable instructions to receive a multi-dimensional input tensor including at least a first dimension, a second dimension and a plurality of channels. The processor is also configured to process the received multi-dimensional input tensor by passing the received multi-dimensional input tensor through the plurality of sequential encoder down-sampling blocks of the learning network, and to generate an output tensor in response to processing the received multi-dimensional input tensor. The output tensor includes at least one segmentation classification.

Abstract: Systems of an electrical vehicle and the operations thereof are provided. A vehicle control system of a vehicle determines a range of lane change initiation times. For a plurality of lane change initiation times in the range of lane change initiation times the vehicle control system determines a set of reachable states. The set of reachable states comprises a plurality of reachable states. For one or more reachable states of the plurality of reachable states the vehicle control system computes an ensuing trajectory. The vehicle control system compares each of the computed ensuing trajectories. Based on the comparison of the computed ensuing trajectories the vehicle control system determines an optimal lane change maneuver. The vehicle control system executes the optimal lane change maneuver by controlling the vehicle from a first lane to a second lane.

Abstract: Systems and methods for controlling a vehicle are described. A processor of a longitudinal planning system determines a state of the vehicle. The processor determines a state of a leader vehicle. The processor, based on the determined state of the vehicle and the determined state of the leader vehicle, determines a critical distance for the vehicle. The processor compares a distance between the vehicle and the leader vehicle with the critical distance. The processor, based on the comparison, determines whether the vehicle is too close to or too far from the leader vehicle. The processor, based on the determination, applies one or more of overshoot constraints, undershoot constraints, and critical constraints. After applying the one or more of overshoot constraints, undershoot constraints, and critical constraints, the processor determines a target acceleration for the vehicle. The processor controls the vehicle to track the target acceleration for the vehicle.

Abstract: A method for controlling a vehicle using a model predictive controller generating consecutive sets of reference states at a calculation frequency. In one embodiment, the method comprises: receiving a trajectory reference for guiding movement of the vehicle; generating, in a calculation cycle repeated at the calculation frequency, a set of reference states based on an initial state of the vehicle at a start time of the calculation cycle and the trajectory reference; sending the set of reference states to a second controller; detecting, by the second controller at a detection frequency equal to or higher than the calculation frequency, an updated state of the vehicle; generating a vehicle control parameter value based on the updated state of the vehicle and a reference state of the set of reference states; and controlling the vehicle using the vehicle control parameter value.

Abstract: An apparatus and a method for controlling a velocity of an autonomous driving vehicle is provided. The method includes steps of: obtaining information of an environment surrounding the vehicle when an obstacle is detected to be on a planning path of the vehicle; obtaining an initial reference velocity profile of the vehicle; determining a safety factor based on the initial reference velocity profile, the information of the environment and information of the vehicle, wherein the safety factor at least comprises a safety distance between the vehicle and the obstacle for the vehicle to follow the obstacle; determining an optimized reference velocity profile based on the information of the environment, the information of the vehicle and the safety factor; and performing the step of determining the safety factor by using the optimized reference velocity profile as the initial reference velocity profile and the step of determining the optimized reference velocity iteratively.

Abstract: Systems of an electrical vehicle and the operations thereof are provided. A vehicle control system of a vehicle determines a range of lane change initiation times. For a plurality of lane change initiation times in the range of lane change initiation times the vehicle control system determines a set of reachable states. The set of reachable states comprises a plurality of reachable states. For one or more reachable states of the plurality of reachable states the vehicle control system computes an ensuing trajectory. The vehicle control system compares each of the computed ensuing trajectories. Based on the comparison of the computed ensuing trajectories the vehicle control system determines an optimal lane change maneuver. The vehicle control system executes the optimal lane change maneuver by controlling the vehicle from a first lane to a second lane.

Abstract: Methods and systems for autonomously steering a moving vehicle are disclosed. A processor determines a longitudinal velocity, a longitudinal acceleration, a lateral acceleration, and a yaw rate of the vehicle. The processor estimates, based on the longitudinal velocity, lateral acceleration, and yaw rate of the vehicle, a change in lateral velocity over time. The processor estimates, based on the change in the lateral velocity over time, the yaw rate, a distance between the front axle of the vehicle and a center of gravity of the vehicle, and a distance between the rear axle of the vehicle and the center of gravity of the vehicle, a lateral front velocity of the vehicle and a lateral rear velocity of the vehicle. Using calculations, a state estimation model for the vehicle is updated by the processor using a lateral acceleration bias. The updated state estimation model is used to autonomously steer the vehicle.

Abstract: An Internet of Things (IoT) technique for vehicular traffic management, including an IoT sensor to measure traffic data of vehicular traffic, a traffic analyzer to determine a traffic event based on the traffic data, and an IoT gateway to issue a response based on the traffic event.

Abstract: Embodiments of the present disclosure provide an apparatus comprising a semiconductor substrate having a first surface, a second surface that is disposed opposite to the first surface, wherein at least a portion of the first surface is recessed to form a recessed region of the semiconductor substrate, and one or more vias formed in the recessed region of the semiconductor substrate to provide an electrical or thermal pathway between the first surface and the second surface of the semiconductor substrate, and a die coupled to the semiconductor substrate, the die being electrically coupled to the one or more vias formed in the recessed region of the semiconductor substrate. Other embodiments may be described and/or claimed.

Abstract: Some of the embodiments of the present disclosure provide a semiconductor package interposer comprising a substrate having a first surface and a second surface, a plurality of vias extending between the first surface and the second surface of the substrate, the plurality of vias electrically connecting electrical connectors or circuitry on the first surface of the substrate to electrical connectors or circuitry on the second surface of the substrate, and metal plugs at least partially filling the plurality of vias. At least one of (i) the first surface or (ii) the second surface of the substrate includes depressions at distal ends of the metal plugs.

Abstract: In an embodiment, there is provided a method of creating a package, the method comprising: providing an initial substrate, wherein the initial substrate comprises a carrier foil, a functional copper foil, and an interface release layer between the carrier foil and the functional copper foil; building up copper portions on the functional copper foil; attaching a chip to a first copper portion; coupling the chip to a second copper portion; encapsulating at least the chip and the copper portions with a mold; and removing the carrier foil and interface release layer.

Abstract: A system including a resistive element of a memory cell and a device to access the resistive element of the memory cell. The resistive element includes (i) a first electrode, and (ii) a second electrode. The device includes (i) a first terminal connected to a first contact, and (i) a second terminal connected to a second contact. One or more of the first contact and the second contact of the device is respectively connected to one or more of the first electrode and the second electrode of the resistive element via a third contact. A size of the third contact decreases from the one or more of the first contact and the second contact of the device to the one or more of the first electrode and the second electrode of the resistive element of the memory cell.

Abstract: A circuit including a first die, an integrated passive device and a second layer. The first die includes a first substrate and active devices. The integrated passive device includes a first layer, a second substrate and passive devices. The second substrate includes vias. The passive devices are implemented at least on the first layer or the second substrate. A resistivity per unit area of the second substrate is greater than a resistivity per unit area of the first substrate. The second layer is disposed between the first die and the integrated passive device. The second layer includes pillars. Each of the pillars connects a corresponding one of the active devices to (i) one of the vias, or (ii) one of the passive devices. The first die, the integrated passive device and the second layer are disposed relative to each other to form a stack.