A crosswalk navigation system for operating an automated vehicle in an intersection includes an intersection-detector, a pedestrian-detector, and a controller. The intersection-detector is suitable for use on a host-vehicle. The intersection-detector is used to determine when the host-vehicle is proximate to an intersection and determine when the intersection includes a cross-walk. The pedestrian-detector is suitable for use on the host-vehicle. The pedestrian-detector is used to determine a motion-vector of a pedestrian relative to the cross-walk. The controller is in communication with the intersection-detector and the pedestrian-detector. The controller is configured to determine a travel-path of the host-vehicle through the intersection, determine when the pedestrian will pass through an intersect-location where the travel-path intersects the cross-walk based on the motion-vector, and operate the host-vehicle to enter the intersection before the pedestrian passes through the intersect-location and to arrive at the intersect-location after the pedestrian passes through the intersect-location.

Vehicle manipulation is performed using occupant image analysis. A camera within a vehicle is used to collect cognitive state data including facial data, on an occupant of a vehicle. A cognitive state profile is learned, on a first computing device, for the occupant based on the cognitive state data. The cognitive state profile includes information on absolute time. The cognitive state profile includes information on trip duration time. Voice data is collected and the cognitive state data is augmented with the voice data. Further cognitive state data is captured, on a second computing device, on the occupant while the occupant is in a second vehicle. The further cognitive state data is compared, on a third computing device, with the cognitive state profile that was learned for the occupant. The second vehicle is manipulated based on the comparing of the further cognitive state data.

Categorizing driving behaviors of other road users includes maintaining a first history of first lateral-offset values of a road user with respect to a center line of a lane of a road; determining a first pattern based on the first history of the first lateral-offset values; determining a driving behavior of the road user based on the first pattern; and autonomously performing, by a host vehicle, a driving maneuver based on the driving behavior of the road user. The first history can be maintained for a predetermined period of time. An apparatus includes a processor that is configured to track a trajectory history of a road user; determine, based on the trajectory history, a driving behavior of the road user; and transmit a notification of the driving behavior.

The disclosure relates to a computer-implemented method for assisting an agent moving in a dynamic environment, in which at least one other agent is present. The method comprises: obtaining sensor information on the environment of the agent; predicting at least one behavior of at least one of the agent or the at least one other agent based on the obtained sensor information; determining at least one human factor relevant for the predicted at least one behavior; adapting the predicted at least one behavior based on the determined human factor; determining whether a communication based on the adapted at least one behavior is beneficial to the agent or an overall traffic objective, e.g., safety; and generating a signal based on the adapted at least one behavior in case of determining that the communication is beneficial, and outputting the generated signal to the agent.

The present teachings provide a method of controlling a vehicle. The method may include maintaining a first history of first lateral-offset values of a road user with respect to a road reference line of a lane of a road. The method includes maintaining a second history of second lateral-offset values of the road user with respect to the road reference line of the road. The method includes determining a first pattern based on the first history of the first lateral-offset values. The method includes determining a second pattern based on the second history of the second lateral-offset values. The method includes adjusting an uncertainty associated with a driving behavior of the road user by comparing the first pattern and second pattern.

A system for detecting hazards for a vehicle includes a vehicle sensor for determining information about an environment surrounding the vehicle and a global navigation satellite system (GNSS). The system also includes a controller in electrical communication with the vehicle sensor and the GNSS. The controller is programmed to perform a plurality of measurements. The controller is further programmed to determine a plurality of classification scores of the first remote vehicle based at least in part on the plurality of measurements of the first remote vehicle and to determine an overall hazard score of the first remote vehicle based at least in part on the plurality of classification scores of the first remote vehicle. The controller is further programmed to take an action based at least in part on the overall hazard score of the first remote vehicle.

An impairment analysis (IA) computer system for detecting an impairment is provided. The IA computer system is associated with a host vehicle, and includes at least one processor in communication with at least one memory device. The at least one processor is programmed to: (i) interrogate or otherwise scan a target vehicle by using a plurality of sensors included on a host vehicle to scan the target vehicle and a target driver; (ii) receive sensor data including target driver data and target vehicle condition data; (iii) analyze the sensor data by applying a baseline model to the sensor data; (iv) detect an impairment of the target driver or target vehicle based upon the analysis; and/or (v) output an alert signal to a host vehicle controller, or direct collision preventing actions (such as automatically engage vehicle safety systems), based upon the determination that the target driver or target vehicle is impaired.

A computer-implemented method for reducing a risk of a collision of a road user with a rear-end of a vehicle. The method includes obtaining first data indicative of a position of the road user in a zone behind the rear-end of the vehicle, obtaining second data indicative of the road user being a vulnerable road user, and providing a control instruction for controlling the vehicle for reducing the risk of the rear-end collision based on the first data and the second data.

An impairment analysis (IA) computer system for detecting an impairment is provided. The IA computer system is associated with a host vehicle, and includes at least one processor in communication with at least one memory device. The at least one processor is programmed to: (i) interrogate or otherwise scan a target vehicle by using a plurality of sensors included on a host vehicle to scan the target vehicle and a target driver; (ii) receive sensor data including target driver data and target vehicle condition data; (iii) analyze the sensor data by applying a baseline model to the sensor data; (iv) detect an impairment of the target driver or target vehicle based upon the analysis; and/or (v) output an alert signal to a host vehicle controller, or direct collision preventing actions (such as automatically engage vehicle safety systems), based upon the determination that the target driver or target vehicle is impaired.

Methods and systems for detecting and responding to impaired drivers are disclosed. The methods intelligently alert and route a user driver's vehicle to improve driving safety based on a predicted path of an impaired driver. The method shares data collected by vehicles in an area to determine which that a vehicle has an impaired driver. The method associates cones of certainty with such a vehicle to establish where the vehicle is likely to travel in the future. If the system determines there is a likelihood of accident based on a proximity to these cones of certainty, an alert can be presented to a user driver of a proximate vehicle. The method also generates routes that avoid or limit the user driver's exposure to the impaired driver's vehicle, such as by suggesting detours or automatically controlling the user driver's vehicle to avoid the path of the impaired driver.