A through the road (TTR) hybridization strategy is proposed to facilitate introduction of hybrid electric vehicle technology in a significant portion of current and expected trucking fleets. In some cases, the technologies can be retrofitted onto an existing vehicle (e.g., a truck, a tractor unit, a trailer, a tractor-trailer configuration, at a tandem, etc.). In some cases, the technologies can be built into new vehicles. In some cases, one vehicle may be built or retrofitted to operate in tandem with another and provide the hybridization benefits contemplated herein. By supplementing motive forces delivered through a primary drivetrain and fuel-fed engine with supplemental torque delivered at one or more electrically-powered drive axles, improvements in overall fuel efficiency and performance may be delivered, typically without significant redesign of existing components and systems that have been proven in the trucking industry.

An apparatus for identifying a weather condition in surroundings of a vehicle, which includes an illuminating unit for illuminating the surroundings, a control unit for controlling the illuminating unit, an image capturing unit for capturing image information of the surroundings, an evaluation unit for evaluating the image information, image data provided by the image capturing unit being compared with predefined reference image data, each characterizing different weather conditions, and a weather condition signal being generated, depending on a match of the comparison, the control unit including a pulse modulation device, with the aid of which the illuminating unit is controlled in such a way that the illuminating unit emits pulsed light signals

The disclosure relates to the process of ascertaining an input variable of a vehicle actuator using a model-based predictive control. According to one exemplary arrangement, a processor unit is designed to access trajectory information and a state data set, which represents a state of surroundings of a vehicle and/or the state of the vehicle and/or a driving state of the vehicle, by an interface. The processor unit carries out a secondary condition algorithm in order to calculate a secondary condition and an MPC algorithm for a model-based predictive control. By carrying out the secondary condition algorithm, a secondary condition is ascertained for the MPC algorithm on the basis of the trajectory information and on the basis of the state data set. By carrying out the MPC algorithm, an input variable is ascertained for an actuator of the vehicle on the basis of the secondary condition. This is carried out in particular such that in a future predicted trajectory, the vehicle follows the specified trajectory with a specified degree of reliability.

The technology relates to using on-board sensor data, off-board information and a deep learning model to classify road wemess and/or to perform a regression analysis on road wetness based on a set of input information. Such information includes on-board and/or off-board signals obtained from one or more sources including on-board perception sensors, other on-board modules. external weather measurement, external weather services, etc. The ground truth includes measurements of water film thickness and/or ice coverage on road surfaces. The ground truth, on-board and off-board signals are used to build the model. The constructed model can be deployed in autonomous vehicles for classifying/regressing the road wetness with on-board and/or off-board signals as the input, without referring to the ground truth. The model can be applied in a variety of ways to enhance autonomous vehicle operation, for instance by altering current driving actions, modifying planned routes or trajectories, activating on-board cleaning systems, etc.

Systems and methods automate the design and execution of randomized experiments. Portions of a field are planted using an agricultural vehicle configured to randomly vary planting parameters when planting a portion of the field. A resulting crop outcome across each portion or sub-portion of the field is observed. A training set of data is generated that includes the varied planting parameters and the associated crop outcomes for each portion of the field. A machine-learned model is trained using the training set of data and is configured to predict a crop outcome for a portion of the field based on historical and forecast conditions and a set of planting parameters applied to a portion of the field. For subsequent iterations, for a target portion of the field, the machine-learned model can be applied to identify a set of planting parameters for planting the target portion of the field to optimize a desired crop outcome.

A drive assist apparatus configured to set a drive condition of a vehicle based on a risk map generated by giving a risk potential to a risk object that is present around the vehicle, includes one or more processors and one or more memories connected to the one or more processors to be able to communicate with the one or more processors. The one or more processors is configured to execute a process including: obtaining information on a surrounding environment of the vehicle; obtaining information on an external environmental factor that may cause deviation of a drive track of the vehicle; and expanding a setting range of the risk potential of the risk object which is positioned in a direction of the expected deviation based on the information on the external environmental factor.

Autonomous driving of a vehicle in which computerized perception by the vehicle, including of its environment and of itself (e.g., its egomotion), is used to autonomously drive the vehicle and, additionally, can also be used to provide feedback to enhance performance, safety, and/or other attributes of autonomous driving of the vehicle (e.g., when certain conditions affecting the vehicle are determined to exist by detecting patterns in or otherwise analyzing what is perceived by the vehicle), such as by adjusting autonomous driving of the vehicle, conveying messages regarding the vehicle, and/or performing other actions concerning the vehicle.

A vehicle is autonomously driven in an environment where a first road for vehicles running in a first direction and a second road for vehicles running in a second direction, different from the first direction, are provided alongside each other, and where a first parking space that is entered from the first road and exited to the first road is set and a second parking space that is entered from the second road and exited to the second road is set. When the vehicle is temporarily parked in the first parking space and a running direction of the vehicle heading for the next destination after being parked is the second direction, the vehicle is transferred from the first parking space to the second parking space by autonomous driving after a user of the vehicle gets out of the vehicle.

Disclosed is a simulation learning-based drowsy driving simulation platform system for detecting careless driving in conjunction with deep learning, the simulation learning-based drowsy driving simulation platform system comprising: a drive state warning device configured to determine a driver's careless driving from a captured image, determine a driver's careless driving determination level, and output the determined level; a smart cruise control interworking part configured to transmit the driver's careless driving determination level outputted from the drive state warning device; and a smart cruise control processing part configured to control a vehicle according to the driver's careless driving determination level transmitted by the smart cruise control interworking part, during a smart cruise control operation.

Methods and systems for a powertrain power management in a vehicle with an electric motor, and an engine are disclosed. The methods and systems involve a powertrain that is operatively coupled to the engine and the electric motor, and an optimizer module operatively coupled to the powertrain. The optimizer module receives an operator information to travel a route from a remote management module, receives current route information for the route from a mapping application in response to the operator information, measures current vehicle status information for the hybrid vehicle, and decides a power management strategy for the vehicle based on the current route information and the current vehicle status information.