To implement a configuration to calculate a manual driving recoverable time required for a driver who is executing automatic driving in order to achieve a requested recovery ratio (RRR) for each road section, and issue a manual driving recovery request notification on the basis of the calculated time. A data processing unit is included, which calculates a manual driving recoverable time required for a driver who is executing automatic driving in order to achieve a predefined requested recovery ratio (RRR) from automatic driving to manual driving and determines notification timing of a manual driving recovery request notification on the basis of the calculated time. The data processing unit acquires the requested recovery ratio (RRR) for each road section set as ancillary information of a local dynamic map (LDM), and calculates the manual driving recoverable time for each road section scheduled to travel, using learning data for each driver.

A computer generates an accident risk definition model to estimate a probability of hazard occurrence as an accident risk by inputting first in-vehicle sensor data collected in the past and hazard occurrence data having information on hazard occurrence from the first in-vehicle sensor data preset therein, generates accident risk estimation data by inputting second in-vehicle sensor data collected in the past to the accident risk definition model and estimating the probability of the hazard occurrence, generates an accident risk prediction model to predict the accident risk after a predetermined time by inputting first biological index data corresponding to the second in-vehicle sensor data and the accident risk estimation data, calculates second biological index data from second biological sensor data by acquiring the second biological sensor data of a driver, and predicts the accident risk after the predetermined time by inputting second biological index data to the accident risk prediction model.

Route planning for a hybrid electric vehicle (HEV) includes obtaining respective engine activation actions for at least some road segments of a route between an origin and a destination by optimizing for at least one of a noise level or energy consumption of an engine of the HEV that is used to charge a battery of the HEV. The HEV is then controlled to follow the at least some of the road segments of the route and to activate the engine according to the respective engine activation actions. Controlling the HEV to follow the at least some of the road segments includes masking at least one of the respective engine activation actions for a current road segment by increasing a volume of an entertainment system of the HEV.

A system for a vehicle includes a powertrain configured to propel the vehicle, and a controller configured to, during a first lap of the vehicle around a track, identify a portion of the track corresponding to a correlation of velocity, throttle position, and steering angle values indicative of a maximum power threshold, and, during a second lap, responsive to approaching the portion, limit power output by the powertrain causing temperature of the powertrain to fall and, upon entering the portion, increase power output to the maximum power threshold causing the temperature to rise, such that a difference in temperature between initiation of the limiting and exiting of the portion approaches zero.

A database of high risk locations is formed and high risk causal factors for the high risk locations determined. Driver behavior is monitored at the sites in the database using data collection devices such as electronic logging devices or mobile phones to see if the drivers exhibit the same specific behaviors that are considered contributing factors to specific accident types at risk of occurrence at those sites. Warnings are provided to drivers approaching the specific sites to prompt behavioral changes which may further be monitored by the data collection devices.

A route risk mitigation system and method using real-time information to improve the safety of vehicles operating in semi-autonomous or autonomous modes. The method mitigates the risks associated with driving by assigning real-time risk values to road segments and then using those real-time risk values to select less risky travel routes, including less risky travel routes for vehicles engaged in autonomous driving over the travel routes. The route risk mitigation system may receive location information, real-time operation information, (and/or other information) and provide updated associated risk values. In an embodiment, separate risk values may be determined for vehicles engaged in autonomous driving over the road segment and vehicles engaged in manual driving over the road segment.

A method, apparatus and computer program product are provided for improving user experiences for autonomous driving. In context of a method, the method determines one or more autonomous transition region parameters for a respective autonomous transition region along a route. The method also, based on the one or more autonomous transition region parameters, determines whether an action is to be performed by a vehicle in accordance with user preference data associated with a user. The method also causes the vehicle to perform the action in accordance with a determination that the action is to be performed by the vehicle.

Various systems and methods are provided for determining a posture of an occupant of a vehicle. In one embodiment, a method comprises capturing images of an occupant in a vehicle via a vehicle camera, determining a current posture of the occupant and a recommended posture for the occupant based on the captured images and body measurements of the occupant, and outputting a guidance based on a difference between the current posture and the recommended posture. In this way, a comfort of the occupant may be increased by guiding the occupant toward a more ergonomic posture.

The disclosure relates to a method for evaluating a driving behavior, wherein detected driving data of at least one human driver, or detected driving data of at least one automated driving vehicle are obtained, wherein a key performance indicator is determined based on the obtained driving data, wherein both a travel time as well as an energy efficiency and/or emissions efficiency are taken into account in determining the key performance indicator, wherein the determined key performance indicator is provided as an evaluation result.

A method includes identifying mass distribution data of an autonomous vehicle (AV). The mass distribution data is associated with a first load proximate a first distal end of a first axle of the AV and a second load proximate a second distal end of the first axle of the AV. The method further includes determining, based on the mass distribution data, one or more handling maneuver limits for the AV. The method further includes causing the AV to travel a route based on the one or more handling maneuver limits.