A trajectory planning method for an autonomous vehicle includes a lateral displacement planner that plans a preliminary traveling trajectory for an autonomous driving vehicle based on perceptual data, positioning data, and map information, where the preliminary traveling trajectory includes N road points, and each road point includes coordinate information of the road point and allowable lateral error information of the road point. A longitudinal speed planner inherits all or some of the N road points output by the lateral displacement planner and determines speed information of n road points based on traffic road condition information and the perception data to obtain a target traveling trajectory. The speed information is a drivable speed of the autonomous driving vehicle at each of the n road points. The target traveling trajectory includes the n road points, each of the n road points including the drivable speed of the autonomous driving vehicle at the road point.

A method of operating a vehicle having a driver assistance system includes detecting driving parameters pertaining to the vehicle while the vehicle is being driven on a roadway using a sensor system of the vehicle. Objects including road signs, lane indicators, and other vehicles are detected using the sensor system. The objects include at least road signs, lane indicators, and other vehicles on the roadway. A traffic rule pertaining to the roadway is identified using a traffic violation warning and prevention system of the driver assistance system. A traffic situation pertaining to the traffic rule is detected based on the detected objects and the driving parameters. An alert is generated that warns the driver of a potential traffic violation when the traffic situation is detected. Alternatively, the driver assistance system may be configured to take control of the vehicle to prevent violation of the traffic rule.

A vehicle-based safety intervention system receives sensor data collected or generated by an onboard computing system of a vehicle. The sensor data is divided into a plurality of blocks, each of the blocks having a duration. A driver behavioral model is applied to one or more of the plurality of blocks to generate one or more driver behavioral parameters. A trend of the one or more driver behavioral parameters is extracted from the plurality of blocks. Based on the extracted trend, it is determined that a driver's performance when operating the vehicle is unsatisfactory or will be unsatisfactory in the future. A vehicle-based intervention is generated based on the determination that the driver's performance is unsatisfactory or will be unsatisfactory in the future.

The invention relates to a device (20) for assisting a driver of a vehicle to perform physical exercises, comprising:—means (21) for acquiring data representative of a driving context,—means (22) for determining a level of risk, from the acquired data,—means (23) for selecting at least one physical exercise compatible with the level of risk,—means (24) for transmitting a signal indicating to the driver the at least one selected exercise; characterised in that it further comprises—means (21′) for acquiring data representative of a history of exercises performed by said driver, said data coming from a remote storage,—and in that the means (23) for selecting at least one physical exercise also use said data representative of a history in order to select said at least one exercise.

A non-transitory computer readable storage medium has instructions executed by a processor to obtain the relative speed between a first traffic object and a second traffic object. The separation distance between the first traffic object and the second traffic object is received. The relative speed and the separation distance are combined to form a quantitative measure of hazard encountered by the first traffic object. The obtain, receive and combine operations are repeated to form cumulative measures of hazard associated with the first traffic object. The cumulative measures of hazard are analyzed to derive a first traffic object safety score for the first traffic object.

A method of distributing driving force of a four wheel drive (4WD) eco-friendly vehicle includes determining a first allowable range of driving force for each driving force based on determination of travel stability, determining a second allowable range of driving force for each driving wheel based on system limitations of at least one of the first driving source or the second driving source, determining a range of available driving force of the first driving wheel based on the first allowable range of driving force and the second allowable range of driving force, determining first target driving force of the first driving wheel in consideration of efficiency of the first driving source within the range of available driving force, and determining second target driving force of the second driving wheel based on the first target driving force and requested torque.

Systems and methods are disclosed for collecting driving data from simulated autonomous driving vehicle (ADV) driving sessions and real-world ADV driving sessions. The driving data is processed to exclude manual (human) driving data and to exclude data corresponding to the ADV being stationary (not driving). Data can further be filtered based on driving direction: forward or reverse driving. Driving data records are time stamped. The driving data can be aligned according to the timestamp, then a standardized set of metrics is generated from the collected, filtered, and time-aligned data. The standardized set of metrics are used to grade the performance the control system of the ADV, and to generate an updated ADV controller, based on the standardized set of metrics.

A vehicle control device includes an acceleration rate feedback processor, a target driving force processor, a requested torque processor, a supercharging determination torque processor, a requested speed processor, and a supercharging operation determination processor. The requested torque processor calculates requested torque of an engine on the basis of target torque obtained from a target driving force and a transmission ratio of a transmission. When determining the requested torque is in the vicinity of supercharging determination torque, the acceleration rate feedback processor inhibits calculation of an amount of acceleration rate feedback. When determining that the requested torque is in the vicinity of the supercharging determination torque, the requested speed processor corrects a target speed in accordance with an altitude, to set a requested speed at a smaller value than on flat ground as the altitude becomes higher.

An automotive electronic dynamics control system for an automated motor-vehicle. The electronic dynamics control system is designed to implement two distinct Model Predictive Control (MPC)-based Trajectory Planners comprising a Longitudinal Trajectory Planner designed to compute a planned longitudinal trajectory for the automated motor-vehicle; and a Lateral Trajectory Planner designed to compute a planned lateral trajectory for the automated motor-vehicle. The electronic dynamics control system is further designed to cause the planned longitudinal trajectory to be computed before the planned lateral trajectory.

A fuel efficiency score management device includes: an acquisition section configured to acquire driving information as detected during driving by a sensor unit installed at a vehicle; a fuel efficiency scoring section configured to decide a fuel efficiency score corresponding to a reduction in carbon dioxide emissions based on the acquired driving information; a score storage section configured to store the fuel efficiency score; and an incentive awarding section configured to lower a usage fee for the vehicle according to the stored fuel efficiency score.