Methods and systems are provided for improving engine restart operations in a hybrid vehicle. While an engine is cranked via motor torque, engine fueling is delayed until the engine speed is sufficiently high and the engine has been rotating in a forward direction continuously for a threshold duration. A disconnect clutch between the engine and motor is slipped while the engine cranks, the clutch engaged only after the engine has run up to the motor speed.

A system providing remaining driving information of a vehicle based on user behavior includes a detection unit, a memory unit and a computation unit. The system stores information acquired by the detection unit during a moving progress of the vehicle to the memory unit to serve as history information, and accordingly generates a personalized model. The computation unit acquires current remaining energy information and at least one set of real-time information through the detection unit, inputs the same to the personalized model, and outputs a predictive remaining driving information to a display interface. The personalized model is generated based on user habits and behavior of various users, used vehicle and driving environment, and is thus capable of generating the personalized predictive remaining driving information. Accordingly, the personalized model integrating various personal factors, vehicle parameters and environment parameters can provide more accurate predictive information for reference of a user.

Systems and methods for starting an engine of a hybrid vehicle are described. In one example, the method starts an engine according to vehicle conditions that are within a range that is defined by one or more thresholds. The thresholds may be adjusted based on a history of individual driving patterns.

An automated method of controlling a speed of a vehicle includes identifying parameters of a driving instance of the vehicle; identifying a predetermined profile that is applicable to the driving instance based on the identified parameters; identifying a predetermined speed policy applicable to the driving instance based on the identified profile; and implementing the identified speed policy during the driving instance. The method may be repeated during the driving instance, whereby the speed policy that is implemented is automatically updated when one or more changes in the identified parameters cause a different predetermined speed policy to be identified. Parameter may include driver parameters (e.g., driver age and driver experience); vehicle parameters (e.g., vehicle age, mileage, and tire wear) tire maintenance information); behavior parameters (e.g., speed, acceleration, hard braking of the vehicle, following distance, swerving, and cornering); and circumstance parameters (e.g., time of day, road information, inclement weather, and traffic congestion).

The invention is notably directed to a computer-implemented method of steering an automated vehicle on a ground of a designated area using a set of one or more offboard sensors (110), each being a 3D laser scanning Lidar. The method comprises repeatedly executing algorithmic iterations. Each iteration of the algorithmic iterations comprises obtaining, for each sensor of the one or more offboard sensors, a grid, which is a 2D occupancy grid of cells reflecting a perception of said each sensor. This is achieved by first accessing a dataset capturing a point cloud model of an environment of said each sensor and processing the dataset to identify characteristics of rays (R.sub.i) emitted by said each sensor, the characteristics including hit points (HP.sub.i) of the rays, as well as projections of the hit points and the rays on a plane (G) corresponding to the ground. The grid is populated by determining a state of each cell (C.sub.j) of the cells based on the identified characteristics, whereby, given a first height (h.sub.1) above the plane and a second height (h.sub.2) above the first height, a necessary and sufficient condition for said each cell to be in an occupied state is to be matched by a projection of a hit point located above the first height, and a necessary condition for said each cell to be in a free state is to be crossed by a projection of an overhanging ray that has dropped below the second height when passing over said each cell. Eventually, a vehicle trajectory is determined based on the grid obtained for said each sensor and the trajectory is forwarded to a drive-by-wire system of the automated vehicle. The invention is further directed to related systems and computer program products.

The invention is notably directed to a computer-implemented method of steering an automated vehicle in a designated area using a set of one or more offboard sensors. Each of these sensors is preferably a 3D laser scanning Lidar, e.g., an infrastructure-based Lidar. The method comprising repeatedly executing algorithmic iterations, wherein each iteration comprises obtaining (S30) a grid, performing (S200) a revision procedure to revise the grid, and determining (S90) a trajectory for the automated vehicle, based on the revised grid. The grid is obtained (S30) as a 2D occupancy grid of cells. This is achieved by determining a state of each cell in accordance with a perception of the one or more offboard sensors. The aim of the revision procedure (S200) is to revise the obtained grid. The grid is revised by correcting the state determined for each of one or more of the cells based on a history of such a cell. Eventually, the method determines (S90) a trajectory for the automated vehicle, based on the revised grid, and forwards (S100) the determined trajectory to a drive-by-wire system of the automated vehicle, to steer the latter. The invention is further directed to related systems and computer program products.

In examples provided herein, a system in a vehicle comprises a processor and a memory including instructions executable by the processor to aggregate and transmit to a context- aware platform (CAP) diagnostic data for a vehicle; receive from the CAP responsive information based on analysis of the diagnostic data; and cause the responsive information to be audibly provided to a driver of the vehicle.

Systems and methods for determining trust across mobility platforms are provided. In one embodiment, a method includes receiving first mobility data for a first automation experience of a user with a first mobility platform. The method also includes receiving a swap indication for a second automation experience of the user with a second mobility platform after the first automation experience. The method further includes selectively assigning the first mobility platform to a first mobility category and the second mobility platform to a second mobility category different than the first mobility category. The method yet further includes calculating an estimated trust score for the second automation experience by applying a trust model based on the first mobility category, the second mobility category, and a sequence of the first automation experience and the second automation experience. The method includes modifying operation of the second mobility platform based on the estimated trust score.

A method and a system augment or improve a distance until charge (DUC) estimation for a vehicle such as a plug-in hybrid electric vehicle (PHEV) by using location information. Such location information may be provided by a global positioning system (GPS) or the like associated with the vehicle. The method and the system generally estimate the DUC value as a function of past driving pattern historical data that is relevant to a current driving situation. To this end, the method and the system ignore past driving pattern historical data that is not relevant to the current driving situation when estimating the DUC value.

A system for determining a driver fuel score an input interface and a processor. The input interface is to receive a fuel efficiency measure for a vehicle or a vehicle type for a plurality of drivers. The processor is to determine a relative fuel performance score based at least in part on the relative fuel performance and determine a driver performance score based at least in part on the relative fuel performance.