A system includes a motion sensor system configured for deployment in a cab of a vehicle and to generate substantially in real-time a digital mapping of driver movement during operation of the vehicle. A computer is configured for deployment in the cab and coupled to the motion sensor system. A driver behavior detector is coupled to the computer and configured to detect a driver distraction event using the driver movement mapping. A communication device at the vehicle is configured to communicate an alert message to one or both of a user interface device in the cab and a remote system in response to the detected driver distraction event.

A system and method for predicting whether a driver of a host vehicle or a remote vehicle intends to make a left or right turn or travel straight through an intersection before the host vehicle or remote vehicle reaches the intersection that relies on a probability model that employs a dynamic Bayesian network. The method includes obtaining a plurality of environmental cues that identify external parameters at or around the intersection, where the environmental cues include position and velocity of the remote vehicle, and obtaining a plurality of host vehicle cues that define operation of the host vehicle. The method then predicts the turning intent of the host vehicle and/or remote vehicle at the intersection using the model based on both the external cues and the vehicle cues using the model. The model can use learned information about previous driver turns at the intersection.

Method and system are provided for vehicle accident avoidance carried out with respect to a host vehicle by modeling behavior. The method includes: monitoring a surrounding environment of the host vehicle and detecting other vehicles in a vicinity of the host vehicle by at least one visual sensor. The method further includes: estimating a speed and direction of each of the detected vehicles; calculating one or more projected paths of each of the detected vehicles based on their current estimated speed and direction, the current monitored surrounding environment, and other vehicle projected paths; estimating a probability of intersection of each projected path with the host vehicle; and providing an alert or action to the host vehicle if there is a high probability of intersection.

In a driving support system of a vehicle, there is provided a structure that, even during operating a control based on a machine input, a driver's sense of incongruity can be reduced as much as possible and/or the steering of the driver be can reflected. The vehicle is equipped with a steering assist mechanism and a braking-driving force distribution mechanism of right and left wheels. In operations of the inventive device, a steering assist torque, applied by the steering assist mechanism, is controlled to a target value determined with reference to a target steering torque to achieve the target route, determined without depending on steering of a driver, and a driver's steering torque; and a braking-driving force difference between the right and left wheels is controlled to a target value based on a steering angle of the driver.

Examples are disclosed for systems and methods for increasing a performance of a driver monitoring system (DMS) based on contextual information within a cabin of the vehicle. In one embodiment, a method comprises, during operation of a vehicle by a driver, collecting vehicle environment status information inside a cabin of the vehicle, the vehicle environment status information related to environmental factors that may impact a cognitive load of the driver and including at least one of information regarding one or more passengers of the vehicle; and information from one or more dashboard controls of the vehicle; and based on the vehicle environment status information and an estimated physiological state of the driver, adjusting one or more environmental controls of the vehicle.

In a first arousal level estimation device according to this invention, an initial residual setup unit sets a residual to be a reference of a driver, i.e., a residual with a smallest value during an initialization period as an initial residual among residuals calculated by a residual calculation unit during the initialization period from timing of starting an operation of a vehicle or timing of a change in a traveling scene including a condition of traveling road of the vehicle as a starting point till a predetermined period of time elapses. An arousal level estimation unit estimates a driver's arousal level based on a result of comparison between the initial residual set by the residual setup unit and the residual calculated by the residual calculation unit after the initialization period has elapsed. In this way, an estimation accuracy relating to a driver's state including a driver's arousal level is improved.

Apparatus for controlling a land vehicle which is self-driving or partially self-driving, comprising a coarse tuning assembly (1, 2, 3) and a fine tuning assembly (4), the coarse tuning assembly (1, 2, 3) comprising: (a) a sensor interface (1) which measures kinematic parameters including speed and braking, (b) fuzzy descriptions which model guidance, navigation and control of the vehicle, and which include: (i) driver behavior and driving dynamics, (ii) uncertainties due to weather, road conditions and traffic, and (iii) input faults including mechanical and electrical parts, and (c) an adaptive fuzzy logic controller (3), and the fine tuning assembly (4) comprising: (a) inputs from the coarse tuning assembly (1, 2, 3), (b) precognition horizons determining how many future samples of input sensor information are required for an optimum control sequence, (c) a linearized multi-input multi-output regression model extracted from the adaptive fuzzy logic controller (3), and (d) a non-linear dynamic linearized regression controller (4a).

A spatial position of at least one first body part, arranged within a sensing range of a sensing device of the motor vehicle, of a vehicle occupant is sensed with respect to the motor vehicle by the sensing device. An anthropometric data model is provided with data including the dimensions of predetermined body parts of a person and the positioning of the body parts with respect to one another. As a result, a spatial position of at least one second body part of the same vehicle occupant which is arranged outside the sensing range of the sensing device can be determined, taking into consideration the sensed spatial position of the first body part and the anthropometric data model. At least one functional unit of the motor vehicle is actuated taking into consideration the sensed spatial position of the second body part.

Systems and methods are provided for determining vehicle configurations. The system can receive simulation data or actual driving data of a driving track corresponding to a driver and associate the simulation data or actual driving data with one or more driving parameters. A driver style can be determined based on the one or more driving parameters. A vehicle configuration can be determined for a vehicle of the driver based on the determined driver style. The system can display one or more vehicle settings associated with the vehicle configuration on a user interface.

The disclosure relates to the technical field of autonomous driving, and provides a method for determining a vehicle driving trajectory, a computer device, and a vehicle, to solve the problem of improving the reliability and safety in planning vehicle driving trajectories. The method includes: sequentially simulating, based on an interaction scenario of a vehicle at a current moment by using forward driving of the vehicle as a constraint condition, an interaction scenario of the vehicle at each of a plurality of future moments, to form a scenario tree consisting of the interaction scenarios at the current moment and the future moments; obtaining a scenario value of each interaction intention in the interaction scenario at each moment in the scenario tree; obtaining an optimal interaction intention of the vehicle in the interaction scenario at each moment based on the scenario value; and determining a driving trajectory of the vehicle based on the optimal interaction intention. According to the disclosure, the optimal interaction intention of the vehicle can be made more consistent with correct driving habits of humans, thereby ensuring the autonomous driving safety and reliability of the vehicle.