Disclosed herein are system, method, and computer program product embodiments for determining objects that are kinematically capable, even if non-compliant with rules-of-the-road, of affecting a trajectory of a vehicle. The computing system (e.g., perception system, etc.) of a vehicle may generate a trajectory for the vehicle and a respective trajectory for each object of a plurality of objects within a field of view (FOV) of the sensing device associated with the vehicle. The computing system may identify objects of the plurality of objects with trajectories that intersect the trajectory for the vehicle and remove from such objects, objects with trajectories that at least one of exit the FOV or intersect with other objects of the plurality of objects within the FOV. The computing system may select, from remaining objects with trajectories that intersect the trajectory for the vehicle, objects with trajectories that indicate a respective collision between the object and the vehicle and assign a severity of the respective collision.

A cruise control method for a hybrid vehicle is provided. The method includes detecting a preceding vehicle and estimating the speed of the preceding vehicle from the information input from a preceding vehicle detecting unit in the on state of a cruise mode and a PnG mode. An upper limit target vehicle speed and a lower limit target vehicle speed are determined from the estimated speed of the preceding vehicle. The driving source of the vehicle is operated to alternately repeat the acceleration (pulse phase) and deceleration (glide phase) of the vehicle between the determined upper limit target vehicle speed and lower limit target vehicle speed.

A method of maintaining stability of a motor vehicle having a first axle, a second axle, and a steering actuator configured to steer the first axle includes determining localization and heading of the vehicle. The method also includes determining a current side-slip angle of the second axle and setting a maximum side-slip angle of the second axle using the friction coefficient at the vehicle and road surface interface. The method additionally includes predicting when the maximum side-slip angle would be exceeded using the localization, heading, and determined current side-slip angle as inputs to a linear computational model. The method also includes updating the model using the prediction of when the maximum side-slip angle would be exceeded to determine impending instability of the vehicle. Furthermore, the method includes correcting for the impending instability using the updated model and the maximum side-slip angle via modifying a steering angle of the first axle.

A control device is provided, which includes: a destination determining unit configured to determine a destination of a hybrid vehicle that includes an engine, a motor and a battery and is able to supply waste heat from the engine to the battery; a arrival judging unit configured to judge whether the hybrid vehicle can arrive at the destination with a remaining capacity of the battery based on the remaining capacity and a temperature of the battery; and a vehicle control unit configured to control the hybrid vehicle to start the engine and supply the waste heat from the engine to the battery when the arrival judging unit judges that the hybrid vehicle cannot arrive at the destination.

The invention provides a method for controlling braking of a vehicle (1) driving along a downhill portion of a road, the vehicle comprising a propulsion arrangement (2, 3), for the propulsion of the vehicle, the method comprising dividing the road portion into a plurality of sections (RS0-RS2), the sections comprising a first section (RS1), and a second section (RS2) following, in the direction of travel of the vehicle, immediately upon the first section (RS1), determining, for the road portion, a road portion control strategy, with a condition that braking on the road portion is done at least partly by means of the propulsion arrangement (2, 3), wherein determining the road portion control strategy comprises determining a speed (SD21), on the second section (RS2), with an aim to minimize the time travelled on the second section, and/or, where the propulsion arrangement comprises an internal combustion engine (2), and a gearbox (3), determining a gear selection (GS2) on the second section (RS2), with an aim to minimize the time travelled on the second section, and wherein determining the road portion control strategy comprises determining, for the first section (RS1), a first section control strategy, with an aim to minimize the time travelled on the first section, and with an aim to provide a vehicle speed at the end of the first section (RS1) which is the same as said determined speed (SD21) on the second section (RS2), and/or to provide a gear selection at the end of the first section which is the same as said determined gear selection (GS2) on the second section (RS2), the method further comprising controlling the vehicle (1) according to the determined road portion control strategy.

A processor unit is configured for determining target torque values (21), which lie within a prediction horizon (20), and target speed values (19), which lie within the prediction horizon (20), by executing an MPC algorithm, which includes a longitudinal dynamics model of a drive train of the motor vehicle. An autonomous driving function of the motor vehicle is carried out in a torque specification operating mode or in a speed specification operating mode as a function of the level of the target torque values (21). In the torque specification operating mode, a prime mover of the drive train is controlled by an open-loop system based on the target torque values (21). In the speed specification operating mode, a speed governor of the drive train is controlled by an open-loop system based on the target speed values (19).

Stochastic nonlinear model predictive control (SNMPC) allows to directly take uncertainty of the dynamics and/or of the system's environment into account, e.g., by including probabilistic chance constraints. However, SNMPC requires the approximate computation of the probability distributions for the state variables that are propagated through the nonlinear system dynamics. This invention proposes the use of Gaussian-assumed density filters (ADF) to perform high-accuracy propagation of mean and covariance information of the state variables through the nonlinear system dynamics, resulting in a tractable SNMPC approach with improved control performance. In addition, the use of a matrix factorization for the covariance matrix variables in the constrained optimal control problem (OCP) formulation guarantees positive definiteness of the full trajectory of covariance matrices in each iteration of any optimization algorithm. Finally, a tailored adjoint-based sequential quadratic programming (SQP) algorithm is described that considerably reduces the computational cost and allows a real-time feasible implementation of the proposed ADF-based SNMPC method to control nonlinear dynamical systems under uncertainty.

A travel support control device creates a travel support plan in which one of travel modes including a CD mode and a CS mode is assigned to each travel section of a travel route based on prediction information generated for the travel route, performs travel support control, and calculates a total distance of electric traveling on the travel route as one result of the travel support control. When a hybrid electric vehicle is traveling in an area outside of communication coverage while the travel support control is being performed, the travel support control device stops switching the travel mode based on the travel support plan and keeps calculating the total distance of electric traveling on the travel route.

A method for managing energy consumption in a vehicle along a first route from a source location to a first target destination includes estimating an energy consumption, E.sub.PC, of the vehicle travelling from the source location to the first target destination along the first route. In case the amount of energy, E.sub.TOTAL, available in the vehicle is not on a level above E.sub.C that ensures arrival of the vehicle at the first target destination according to a first determined level of certainty, an estimated energy consumption, E.sub.AUX, of each auxiliary system on-board the vehicle for the first route, and an energy utilization information, EU.sub.INFO, indicating a priority level of each auxiliary system on-board the vehicle for the first route is obtained. An energy consumption adaption sequence of the auxiliary systems is determined for the first route based on the obtained E.sub.AUX and EU.sub.INFO of each auxiliary system such that E.sub.TOTAL is maintained on a level above E.sub.PC that ensures arrival of the vehicle at the first target destination according to a second determined level of certainty.

Systems and methods are provided for vehicle navigation. In one implementation, a system may comprise at least one processor. The processor may be programmed to receive images representative of an environment of the host vehicle and analyze the images to identify a first object and a second object. The processor may determine a first predefined navigational constraint implicated by the first object and a second predefined navigational constraint implicated by the second object, wherein the first and second predefined navigational constraints cannot both be satisfied, and the second predefined navigational constraint has a priority higher than the first predefined navigational constraint. The processor may determine a navigational action for the host vehicle satisfying the second predefined navigational constraint, but not satisfying the first predefined navigational constraint and, cause an adjustment of a navigational actuator of the host vehicle in response to the determined navigational action.