An information providing device includes an external environment recognition unit that recognizes an external environmental situation of a movable body, an information providing control unit that provides a notification of recommended stopping information based on a current indication of traffic signals recognized by the external environment recognition unit, and a determination unit which determines, in the case that a first intersection and a second intersection are positioned in a travel direction of the movable body, whether or not a degree of proximity of the first intersection and the second intersection satisfies a predetermined condition. In the case it is determined by the determination unit that the predetermined condition is satisfied, the information providing control unit prevents the recommended stopping information based on a current indication of the traffic signal belonging to the second intersection from being provided.

Systems and methods for situational behavior of an autonomous vehicle are disclosed. In one aspect, an autonomous vehicle includes at least one perception sensor configured to generate perception data indicative of at least one other vehicle on a roadway, a non-transitory computer readable medium, and a processor. The processor is configured to determine that the other vehicle is violating one or more rules of the roadway based on the perception data, tag the other vehicle as a non-compliant driver, and modify control of the autonomous vehicle in response to tagging the other vehicle as a non-compliant driver.

Determination of a trajectory for a first vehicle (1) by model predictive control (MPC) is provided. Trajectory information about a second vehicle (18) traveling in the area ahead of the first vehicle (1) is utilized. In particular, discretization points (P.sub.1, P.sub.2, P.sub.3) and arrival times of the vehicles (1, 18) at the discretization points (P.sub.1, P.sub.2, P.sub.3) are utilized to generate constraints for the model predictive control of the first vehicle (1).

A method for traffic light auto-labeling includes aggregating vehicle-to-infrastructure (V2I) traffic light signals at an intersection to determine transition states of each driving lane at the intersection during operation of an ego vehicle. The method also includes automatically labeling image training data to form auto-labeled image training data for a traffic light recognition model within the ego vehicle according to the determined transition states of each driving lane at the intersection. The method further includes planning a trajectory of the ego vehicle to comply with a right-of-way according to the determined transition states of each driving lane at the intersection according to a trained traffic light detection model. A federated learning module may train the traffic light recognition model using the auto-labeled image training data during the operation of the ego vehicle.

A vehicle control method of an autonomous vehicle for a right and left turn at a crossroad includes: determining whether a second vehicle intends to change a lane while passing a front or a rear of a first vehicle in order to move to a target lane for the right and left turn at the crossroad; controlling the first vehicle to decelerate when it is determined that the second vehicle intends to change the lane while passing the front of the first vehicle; determining whether the second vehicle is entering the first lane toward the front or the rear of the first vehicle; calculating a steering amount of the second vehicle when it is determined that the second vehicle is entering the first lane toward the front of the first vehicle; and controlling the first vehicle to decelerate according to the steering amount.

Traveling environment information is recognized. A predicted traveling path is calculated based on a driving condition of a vehicle. An oncoming-vehicle predicted traveling path is calculated based on behavior of an oncoming vehicle. It is determined whether the vehicle has an intention to enter a first intersecting road at an intersection. When the vehicle cannot enter the first intersecting road, the predicted traveling path is corrected to a limit traveling path. It is determined whether the oncoming vehicle has an intention to enter a second intersecting road at the intersection. When the oncoming vehicle cannot enter the second intersecting road, the oncoming-vehicle predicted traveling path is corrected to an oncoming-vehicle limit traveling path. The oncoming vehicle is set as a control target against which emergency braking is executed when the predicted traveling path and the oncoming-vehicle predicted traveling path overlap each other at least in part.

Behavior information input unit receives stop-behavior information about vehicle from automatic-driving control device. Image-and-sound output unit outputs inquiry information for inquiring of an occupant whether a possibility of collision between an obstacle and vehicle is to be excluded from a determination object in automatic-driving control device to notification device, when a distance from one point on a predictive movement route of the obstacle to the obstacle is greater than or equal to a first threshold, and a speed of the obstacle is less than or equal to a second threshold. Operation signal input unit receives a response signal for excluding the collision possibility from the determination object. Command output unit outputs a command to exclude the collision possibility from the determination object to automatic-driving control device.

A vehicle includes a system and method of navigating the vehicle. The system includes a sensor and a processor. The sensor captures an image of a roadway. The processor focuses the sensor at a road segment selected from a plurality of road segments of the roadway using a machine learning program based on a risk of the road segment. The machine learning program is trained to focus the sensor by calculating the risk for each of the plurality of road segments of the roadway based on a hazard probability associated with each road segment and an occupancy probability associated with each road segment, selecting the road segment from the plurality of road segments based on the risk associated with the road segment, and determining a reduction in the risk for a road risk model of the roadway due to selecting the road segment.

There is provided an adaptive cruise control method for autonomously adapting the speed of an ego vehicle (300) to maintain a target headway, headway being distance from the ego vehicle to a forward vehicle (302), the ego vehicle equipped with a perception system (100) for measuring a current headway and a current speed and acceleration of the forward vehicle relative to ego vehicle, the method comprising: in response to detecting that the current headway is below the target headway, determining and implementing a deceleration strategy for increasing to the target headway; wherein the deceleration strategy is determined so as to selectively optimize for comfort in dependence on a predicted headway, the predicted headway computed for a future time instant based on the current speed and acceleration of the forward vehicle relative to the ego vehicle.

Techniques for accurately predicting and avoiding collisions with objects detected in an environment of a vehicle are discussed herein. A vehicle computing device can implement a model to output data indicating costs for potential intersection points between the object and the vehicle in the future. The model may employ a control policy and a time-step integrator to determine whether an object may intersect with the vehicle, in which case the techniques may include predicting vehicle actions by the vehicle computing device to control the vehicle.