A system for an autonomous vehicle that predicts a location-based maneuver of a remote vehicle located in a surrounding environment includes one or more vehicle sensors collecting sensory data indicative of one or more vehicles located in the surrounding environment. The system also includes one or more automated driving controllers in electronic communication with the one or more vehicle sensors. The one or more automated driving controllers execute instructions to compare a lane of travel of the remote vehicle with a current lane of travel of the autonomous vehicle. In response to determining the lane of travel of the remote vehicle is a different lane than the current lane of the autonomous vehicle, the one or more automated driving controllers predict the location-based maneuver of the remote vehicle based on aggregated vehicle metrics that are based on historical data collected at the specific geographical location.

A method is disclosed for analyzing historical accident information to adjust driving actions of an autonomous vehicle over a travel route in order to avoid accidents which have occurred over the travel route. Historical accident information for the travel route can be analyzed to, for example, determine accident types which occurred over the travel route and determine causes and/or probable causes of the accident types. In response to determining accident types and causes/probable causes of the accident types over the travel route, adjustments can be made to the driving actions planned for the autonomous vehicle over the travel route. In addition, in an embodiment, historical accident information can be used to analyze available travel routes and select a route which presents less risk of accident than others.

A vehicle driving assist device includes a traveling environment recognizer, an obstacle recognizer, an emergency collision, an oncoming moving body recognizer, a lateral position calculator, a risk degree calculator, and a preliminary collision avoidance controller. The obstacle recognizer recognizes, based on information recognized by a traveling environment recognizer, an obstacle on a vehicle's traveling path. An emergency collision avoidance controller performs emergency collision avoidance control for avoiding a collision with the obstacle. An oncoming moving body recognizer recognizes, an oncoming moving body in an oncoming lane. A lateral position calculator calculates a distance from a lane marker defining the oncoming lane to a reference position of the oncoming moving body. A risk degree calculator calculates a risk degree for the oncoming moving body. A preliminary collision avoidance controller recognizes the oncoming moving body as the obstacle, and performs preliminary control before the emergency collision avoidance control.

Systems and methods use cameras to provide autonomous navigation features. In one implementation, a method for navigating a user vehicle may include acquiring, using at least one image capture device, a plurality of images of an area in a vicinity of the user vehicle; determining from the plurality of images a first lane constraint on a first side of the user vehicle and a second lane constraint on a second side of the user vehicle opposite to the first side of the user vehicle; enabling the user vehicle to pass a target vehicle if the target vehicle is determined to be in a lane different from the lane in which the user vehicle is traveling; and causing the user vehicle to abort the pass before completion of the pass, if the target vehicle is determined to be entering the lane in which the user vehicle is traveling.

A controller for a hybrid vehicle performs charging control when a shift range of the hybrid vehicle is a first range, and does not perform the charging control when the shift range of the hybrid vehicle is a second range, the charging control being control of charging a power storage device with electric power generated by a generator driven by an engine. The controller records diagnosis information when an SOC of the power storage device is equal to or lower than a first threshold value and the shift range of the hybrid vehicle is the first range, and does not record the diagnosis information when the SOC of the power storage device is equal to or lower than the first threshold value and the shift range of the hybrid vehicle is the second range.

Methods for reversing determination for a vehicle asset are provided. The methods include capturing by a telematics device coupled to the vehicle acceleration data from a three-axis accelerometer, determining by a reversing-determination machine learning mode, a machine-learning-determined reversing indication for the vehicle asset. The reversing-determination machine-learning model being trained by a vehicle reversing indication comprising a vehicle speed and a reverse gear indication.

A method includes detecting, via a processor of an autonomous vehicle, an upcoming downhill road segment of a route on which the autonomous vehicle is currently travelling. The detection is based on map data, camera data, and/or inertial measurement unit (IMU) data. In response to detecting the upcoming downhill road segment, a descent plan is generated for the autonomous vehicle. The descent plan includes a speed profile and a brake usage plan. The brake usage plan specifies a non-zero amount of retarder usage and an amount of foundation brake usage for a predefined time period. The method also includes autonomously controlling the autonomous vehicle, based on the descent plan, while the autonomous vehicle descends the downhill road segment.

A method for operating a vehicle, which can be operated in a manual and an automated driving mode is provided. Depending on a driving situation occurring in the automated driving mode, a take-over request for a user of the vehicle to take over a driving task is issued to the vehicle user. During the automated driving mode the vehicle monitors whether the vehicle user is able to comply with the take-over request at the time of the issued take-over request based on a current seat position of the vehicle user and/or a current seat setting of a vehicle seat of the vehicle user.

A system for user interaction with an automated device includes a control system configured to operate the device during an operating mode corresponding to a first state in which the control system automatically controls the device operation, and the operating mode prescribes that a user monitor the device operation during automated control. The control system is configured to allocate a time period for the device to transition to a temporary state in which automated control is maintained and the user is permitted to stop monitoring and perform a task unrelated to device operation. The system includes a user interaction system including a visual display configured to present trajectory information, an indication as to whether an area is conducive to putting the device in the temporary state, and time period allocation information, the user interaction system including an interface engageable by the user to manage scheduling of allocated time period(s).

A lane change support device includes a control unit configured to execute lane change control for enabling a vehicle to automatically change lanes from a lane in which the vehicle is traveling to an adjacent lane. The control unit counts a holding time for which an operation part that is operated to a predetermined operation position to start the lane change control is continuously held at the operation position, starts the lane change control when the counted holding time reaches a predetermined threshold time, and calculates a proficiency level of a driver of the vehicle for an operation of the lane change support device during execution of the lane change control and sets the threshold time to be used for a successive lane change control based on the proficiency level.