A method for providing a positive decision signal for a vehicle which is about to perform a traffic scenario action. The method includes receiving information about at least one surrounding road user, which information is indicative of distance to the surrounding road user with respect to the vehicle and at least one of speed and acceleration of the surrounding road user; calculating a value based on the received information; providing the positive decision signal to perform the traffic scenario action when the calculated value is fulfilling a predetermined condition. The value is calculated based on an assumption that the surrounding road user will react on the traffic scenario action by changing its acceleration.

A driving assistance device includes a storage device configured to store a program and a processor connected to the storage device. The processor executes the program to recognize a physical object, generate a future avoidance trajectory along which a moving object is able to move while avoiding the contact with the physical object, acquire a steering state of the moving object, determine an amount of change in an avoidance trajectory error, calculate an index value by making a weight of an avoidance trajectory error at a point in time earlier than a reference point in time greater or less than a weight of the avoidance trajectory error at the reference point in time according to the amount of change, and guide a driver of the moving object or the moving object such that the steering state of the moving object is changed in accordance with the index value.

A method for determining a risk boundary in response to the plurality of indications of hard braking events wherein the risk boundary is indicative of a plurality of speed flow pairs at which a risk of a hard braking event is below a threshold value, determining, at a road segment level, a set of speed flow pairs of average speed and vehicle count and a plurality of indications of hard braking events , determining a host vehicle speed, and performing at least one of reducing the host vehicle speed and increasing a host vehicle following distance in response to the host vehicle speed exceeding the risk boundary for the vehicle flow density.

A method includes obtaining sensor data associated with a target object at an ego vehicle, where the sensor data includes image data within an image frame. The method also includes identifying a first longitudinal distance of a first point of the target object from the ego vehicle within a world frame. The method further includes identifying a first lateral distance of the first point of the target object from the ego vehicle within the world frame using a first normalization ratio that is based on the image data. The method also includes predicting a future path of the target object based on the first longitudinal distance and the first lateral distance. In addition, the method includes controlling at least one operation of the ego vehicle based on the predicted future path of the target object.

A control device for collision avoidance assistance includes a processor. The processor is configured to execute region setting processing for setting an assistance determination region indicating a particular region forward of a vehicle, and to execute accumulation processing in which the processor gives an object a determination value and accumulates the determination value. The object is located in the assistance determination region. The determination value is decided according to the position of the object. The processor is configured to perform collision avoidance assistance control of assisting in avoidance of collision of the vehicle and the object based on driving environment information indicating a driving environment of the vehicle, when a cumulative value regarding the object calculated in the accumulation processing exceeds a predetermined threshold value.

Methods and systems are provided for controlling an autonomous vehicle. In one embodiment, a method includes: A method of controlling an autonomous vehicle, comprising: receiving, by a processor, a first set of data obtained from an inertial measurement unit of the vehicle; receiving, by the processor, a second set of data obtained from a global positioning system of the vehicle; receiving, by the processor, a third set of data obtained from a camera of the vehicle; determining, by the processor, at least two vehicle states relative to markings of a lane by processing the first set of data, the second set of data, and the third set of data as measurement with an extended Kalman filter; and controlling, by the processor, the vehicle based on the at least two vehicle states.

Computer-implemented methods, systems and computer program products for facilitating operationally customized access to smart vehicles are provided. Aspects include receiving request to access a smart vehicle. Aspects also include receiving vehicle operation constraints associated with the smart vehicle using a processor. Aspects also include generating a vehicle policy based at least in part on the request to access the smart vehicle and the vehicle operation constraints using the processor. The vehicle policy includes rules for operation of the smart vehicle. Aspects also include transmitting the vehicle policy to the smart vehicle. Aspects also include moderating the operation of the smart vehicle based on at least in part the vehicle policy.

In one embodiment, a method for monitoring a localization function in an autonomous driving vehicle (ADV) can use known static objects as ground truths to determine when the localization function encounter errors. The known static objects are marked on a high definition (HD) map for the real-time driving environment. When the ADV detects one or more known static objects, the ADV can use sensor data, locations of the one or more static objects, and one or more error tolerance parameters to create a localization error tolerance area surrounding a current location of the ADV. The ADV can project the tolerance area on the HD map, performs a localization operation to generate an expected location of the ADV on the HD map, and determines whether the generated location falls within the projected tolerance area. If the generated location falls outside the projected tolerance area, indicating a localization function of the ADV encounter errors, the ADV can generate an alarm to alert a human driver to switch to a manual driving mode. If no human driver is available in the ADV, the ADV can activate a vision-based fail-safe localization procedure.

A vehicle includes a navigation configured to output navigation information; and a controller configured to receive a requested stopping location of the vehicle for a passenger to get on or off, to determine a stopping location of the vehicle based on the navigation information and information on a designated no-stopping zone, in response to the receiving of the requested stopping location, and to control the vehicle to head to the determined stopping location.

A surrounding situation information acquisition device acquires surrounding situation information of a vehicle. A steering angle sensor detects a steering angle and a steering direction of the vehicle. A steering assist controller executes traveling control involving steering assist control based on information output from the surrounding situation information acquisition device and information output from the steering angle sensor. The steering assist controller recognizes, based on the information output from the surrounding situation information acquisition device, an oncoming vehicle in an oncoming lane adjacent to a traveling lane of the vehicle and an avoidance target on a road shoulder side of the traveling lane of the vehicle, estimates an avoidance priority level of the oncoming vehicle and an avoidance priority level of the avoidance target, and sets a new target lane keeping traveling path of the vehicle based on the avoidance priority levels.