A method for determining presence of an object via a vehicular radar sensing system includes providing a radar sensor having a plurality of antennas, which includes a plurality of transmitting antennas and a plurality of receiving antennas. The plurality of antennas includes a plurality of sets of antennas, each set having a V shape or an X shape, and with each of the shaped sets of antennas having an apex. A signal feed is provided to the apex of each of the shaped sets of antennas. A radar beam is transmitted via the plurality of transmitting antennas and side lobes of the transmitted radar beam are reduced via the plurality of shaped sets of antennas. An output of the receiving antennas is communicated to a processor, and the processor determines presence of one or more objects exterior the vehicle and within the field of sensing of the radar sensor.

An actuator system configured to control an operation of a vehicle includes at least one application configured to set a kinematic plan for the vehicle, and one and more processors configured to arbitrate a plurality of kinematic plans including a kinematic plan set by the at least one application.

An actuator system configured to control an operation of a vehicle includes at least one application configured to set a kinematic plan for the vehicle, and one and more processors configured to arbitrate a plurality of kinematic plans including a kinematic plan set by the at least one application.

A method and system of determining whether a stationary vehicle is a blocking vehicle to improve control of an autonomous vehicle. A perception engine may detect a stationary vehicle in an environment of the autonomous vehicle from sensor data received by the autonomous vehicle. Responsive to this detection, the perception engine may determine feature values of the environment of the vehicle from sensor data (e.g., features of the stationary vehicle, other object(s), the environment itself). The autonomous vehicle may input these feature values into a machine-learning model to determine a probability that the stationary vehicle is a blocking vehicle and use the probability to generate a trajectory to control motion of the autonomous vehicle.

An electric powered vehicle may include a maximum speed limiting device. In the first mode, in a case where the distance is shorter than a first reference value, the maximum speed is limited to a value lower than the maximum speed applied when the distance is longer than the first reference value. In the second mode, in a case where the distance is shorter than a second reference value, the maximum speed is limited to a value lower than the maximum speed applied when the distance is longer than the second reference value. In a case where the distance changes from a value shorter than the first and second reference values to a value longer the first and second reference values, the maximum speed limiting device increases the maximum speed at an earlier timing in the second mode than in the first mode.

An apparatus comprising an interface, a memory and a processor. The interface may be configured to receive sensor data samples during operation of a vehicle. The memory may be configured to store the sensor data samples over a number of points in time. The processor may be configured to analyze the sensor data samples stored in the memory to detect a pattern. The processor may be configured to manage an application of brakes of the vehicle in response to the pattern.

Systems and methods for detecting and tracking objects are provided. In one example, a computer-implemented method includes receiving sensor data from one or more sensors. The method includes inputting the sensor data to one or more machine-learned models including one or more first neural networks configured to detect one or more objects based at least in part on the sensor data and one or more second neural networks configured to track the one or more objects over a sequence of sensor data. The method includes generating, as an output of the one or more first neural networks, a 3D bounding box and detection score for a plurality of object detections. The method includes generating, as an output of the one or more second neural networks, a matching score associated with pairs of object detections. The method includes determining a trajectory for each object detection.

An apparatus comprising an interface, a memory and a processor. The interface may be configured to receive sensor data samples during operation of a vehicle. The memory may be configured to store the sensor data samples over a number of points in time. The processor may be configured to analyze the sensor data samples stored in the memory to detect a pattern. The processor may be configured to manage an application of brakes of the vehicle in response to the pattern.

A system and approach for a vehicle system. The vehicle system may include a vehicle, a propulsion device (e.g., a combustion engine or electric motor), and a controller. The propulsion device may at least partially power the vehicle. The controller may be in communication with the propulsion device and may control the propulsion device according to a target speed of the vehicle. The controller may include a model of energy balances of the vehicle and may use the model to estimate energy losses over a travel horizon of the vehicle. The controller may optimize a cost function over the travel horizon of the vehicle based at least in part on the estimated energy losses to set an actual speed for the vehicle. The estimated energy losses may include one or more of aerodynamic drag, vehicle friction, and conversion efficiency from the propulsion device.

Engine control modules as well as methods and systems implementable in a vehicle are disclosed, in which the engine control module includes a processing unit operative to control a target vehicle speed. The processing unit receives current status information and lookahead information regarding a route to be taken by the vehicle, performs a lookahead power requirement calculation based on the current status information and the lookahead information to determine an event, calculates a plurality of offsets with respect to an isochronous speed of the vehicle based on the determined event, and sets a target vehicle speed curve by applying the plurality of offsets to the isochroous speed.