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.

An assistance system for a vehicle capable of automated operation has a controller having a processor and tangible, non-transitory memory on which instructions are recorded. The vehicle is located on a first lane in a vicinity of one or more neighboring vehicles, the first lane merging with a second lane at a merging trajectory location. The controller is adapted to selectively execute a leader determination module when a distance of the vehicle to the merge starting point is less than a threshold value. This includes determining an estimated arrival time of the vehicle to a merge starting point of the merging trajectory location. The controller is adapted to select a leader vehicle from the neighboring vehicles based in part on their respective estimated arrival times to the merge starting point. Operation of the vehicle is controlled based in part on the leader vehicle.

Implementations described herein relate to leveraging corresponding streams of speed readings of a vehicle generated by different speed sensors of different computing devices to automatically determine an updated tire size of tires of the vehicle. For example, while a user of the vehicle is driving, a first stream of speed readings can be generated by a vehicle speed sensor of an in-vehicle computing device of the vehicle and a second stream of speed readings can be generated by a mobile speed sensor of a mobile computing device of the user of the vehicle. Processor(s) can obtain the different streams of speed readings from the different computing devices and process the different streams using various operations to determine the update tire size of the tires of the vehicle. The updated tire size can be subsequently utilized to update operational parameter(s) of the vehicle that influence how the vehicle operates.

A system is provided generating a context-dependent experience for a vehicle driver, e.g., to relax or enhance the driver's mental state. A system controller is configured to receive driving context data regarding a driving situation from various driving context data sources. The driving context data may include vehicle operation data, e.g., generated by vehicle-based sensors, and environmental data regarding an environment external to the vehicle, e.g., received wirelessly from a remote server. The system controller may identify content triggering events based on (a) the received driver context data and (b) a set of content triggering rules. For each content triggering event, the system controller may select one or more human-perceivable contents elements (e.g., audio clips, seat massage settings, or air conditioner settings), and control one or more content output devices (e.g., speakers, seat massage system, or vehicle HVAC system) to output the selected content element(s) to the driver.

The disclosure relates to a control method for driver assistance. The method includes: receiving positioning information of a vehicle; obtaining information about a road structure ahead of the vehicle based on the positioning information; and determining, based on the information about the road structure, whether an alarm for a sharp bend is needed. The disclosure further relates to a control device for driver assistance, a computer storage medium, and a vehicle.

A computer includes a processor and a memory storing instructions executable by the processor to receive data indicating that an electric vehicle is in an enclosed space; in response to receiving the data, instruct window actuators of the electric vehicle to open windows of the electric vehicle; and in response to receiving the data, instruct a heater of the electric vehicle to generate heat.

A parking assistance method is provided. The method includes recognizing image features of a traffic line corresponding to a parking position of a vehicle. A category of the parking position of the vehicle is determined based on the image features of the traffic line. Once the vehicle is determined to be parking illegally based on the category of the parking position of the vehicle; a warning is issued.

A system and method for assisting drivers of vehicles are described. The systems and methods provide an extended view of the area surrounding the driver's vehicle while providing real-time object trajectory for objects and other vehicles that may enter the driver's reactionary zone. The system and methods capture images of the area surrounding the driver's vehicle and create a composite image of that area in real-time and using Augmented Reality (AR) create a 3-D overlay to warn the driver as objects or other vehicles enter the driver's reactionary zone so that a driver can make more informed driving decisions.

Systems and methods for scheduling environment perception-based data offloading for numerous connected vehicles are disclosed. In one embodiment, a method for offloading data includes capturing an image of a view of interest from a vehicle, segmenting the image into a plurality of blocks, and determining a scheduling priority for each of one or more blocks among the plurality of blocks based on block values, wherein the block values relate to one or more objects of interest contained in each of the one or more blocks. The method further includes offloading, from the vehicle to a server, one or more blocks based on the scheduling priority of the one or more blocks.

The present invention discloses an intelligent vehicle platoon lane change performance evaluation method. First, an intelligent vehicle platoon lane change performance test scenario is established; secondly, a three-degree of freedom nonlinear dynamics model is established according to motion characteristics of intelligent vehicles in a platoon lane change process; further, an improved adaptive unscented Kalman filter algorithm is utilized to perform filter estimation on state variables of positions and velocities of platoon vehicles; and finally, based on accurately recursive vehicle motion state parameters, evaluation indexes for platoon lane change performance are proposed and quantified, and an evaluation system for platoon lane change performance is constructed. According to the method proposed in the present invention, the problem of lacking platoon lane change performance quantitative evaluation at present is solved, vehicle motion state parameters can be measured in a high-precision and comprehensive manner, multi-dimensional platoon lane change performance evaluation indexes are quantified and output, and comprehensive, accurate, and reliable scientific quantitative evaluation for platoon lane change performance is achieved.