Example embodiments relate to a method for cut-in identification and classification. An example embodiment includes a obtaining operational data about one or more vehicles; based on the operational data, identifying the presence of one or more cut-ins within the operational data; extracting, from the operational data, cut-in data that depicts one or more of the cut-ins identified within the operational data; and, based on the extracted cut-in data, training a model for controlling an autonomous vehicle. Identifying the presence of a given cut-in includes: determining that at least one vertex of a bounding box surrounding a vehicle was located more than a threshold distance within a lane being navigated by a given vehicle; and determining that the ability of the given vehicle to maintain its course and speed was impeded by the presence of the particular additional vehicle within the lane.

A vehicle control device includes a recognizer configured to recognize a surrounding environment including a structure of a road near a vehicle and another vehicle, a deriver configured to derive a predicted probability that the other vehicle will travel in the future along each of routes which are assumed when a plurality of routes along which the other vehicle is able to travel are assumed on a road on which the other vehicle recognized by the recognizer travels, and a travel controller configured to control behavior of the vehicle based on the predicted probability derived by the deriver.

A vehicle assistive system includes a plurality of context information generating devices, each configured to output context information relating to a mobile vehicle, a processor, and a non-transitory computer-readable medium comprising instructions for performing acts. The acts include: generating one or more object representations based on corresponding context information; predicting one or more future states, each relating to a state of the object representation at a future time; eliminating the future states having a probability that does not meet a corresponding threshold; detecting a future event for one or more of the future states; providing one or more action items for each detected future event; performing one or more actions associated with the action items including an action selected from the group consisting of generating a notification using a notification device, and controlling the mobile vehicle using a control device.

A computer-implemented method is provided that involves determining, based on map data, an approaching merge region comprising an on-ramp merging with a road comprising one or more lanes, wherein a truck is traveling on an initial lane of the road according to a navigation plan. The method involves an indication of movement of a vehicle on the on-ramp, wherein the indication of movement is based on data collected by one or more sensors configured to capture sensor data from an environment surrounding the truck. The method involves determining, for the on-ramp and the one or more lanes, respective avoidance scores indicative of a likelihood of an interaction between the truck and the vehicle based on the approaching merge region. The method involves updating the navigation plan based on the respective avoidance scores. The method also involves controlling the truck to execute a driving strategy based on the updated navigation plan.

A cross-traffic warning system for a motor vehicle includes first and second input devices transmitting associated first and second input signals for first and second detected objects positioned on the roadway. The system further includes a computer having one or more processors and a computer readable medium storing instructions. The processor is programmed to determine that the first object is a Vulnerable Road User (“VRU”) travelling on a first path based on the first input signal. The processor is further programmed to determine that the second object is a third party vehicle and further that the VRU and the third party vehicle are travelling on an associated one of first and second paths to imminently collide with one another based on the first and second input signals. The processor is further programmed to generate an actuation signal in response to the processor determining the imminent collision.

Cautious driving and cautious driving speed determination for an autonomous vehicle is responsive to receiving a non-yield backup prediction for the vehicle regarding a traffic participant in a region of interest in a road network surrounding the vehicle, the non-yield backup prediction including a non-yield probability value for the traffic participant not yielding to the vehicle. Driving information, including speed, for other traffic participants within the region of interest is obtained from a sensor system, and an average speed of the other traffic participants is determined. A driving system determines a cautious driving speed for the vehicle by calculating a reverse probability value, which is a reverse percentage of the non-yield probability value relative to a maximum value for it, and multiplying the average speed of the other traffic participants by the reverse probability value. The driving system controls the vehicle to reduce its speed to the cautious driving speed.

Techniques for determining that a first vehicle is associated with a reverse state, and controlling a second vehicle based on the reverse state, are described herein. In some examples, the first vehicle may provide an indication that the first vehicle will be executing a reverse maneuver, such as with reverse lights on the vehicle or by positioning at an angle relative to a road or parking space to allow for the reverse maneuver into a desired location. A planning system of the second vehicle (such as an autonomous vehicle) may receive sensor data and determine a variety of these indications to determine a probability that the vehicle is going to execute a reverse maneuver. The second vehicle can further determine a likely trajectory of the reverse maneuver and can provide appropriate accommodations (e.g., time and/or space) to allow the second vehicle to execute the maneuver safely and efficiently.

A method and an electronic device for generating a trajectory of a Self-Driving Car (SDC) are provided. The method comprises: determining a presence of at least one third-party object around the SDC; generating a plurality of predicted trajectories for the third-party object, where at least one of the plurality of trajectories includes a maneuver executable, by the third-party object, at a future third-party object location; calculating, for the at least one of the plurality of trajectories including the a respective braking profile associated with the third-party object; in response to the respective braking profile being above a pre-determined threshold, eliminating an associated one of the at least one of the plurality of trajectories from future processing; determining an SDC trajectory based on remaining ones of the plurality of predicted trajectories for the third-party.

During the autonomous driving, the movement trails or moving history of obstacles, as well as, an autonomous driving vehicle (ADV) may be maintained in a corresponding buffer. For the obstacles and the ADV, the vehicle states at different points in time are maintained and stored in one or more buffers. The vehicle states representing the moving trails or moving history of the obstacles and the ADV may be utilized to reconstruct a history trajectory of the obstacles and the ADV, which may be used for a variety of purposes. For example, the moving trails or history of obstacles may be utilized to determine lane configuration of one or more lanes of a road, particularly, in a rural area where the lane markings are unclear. The moving history of the obstacles may also be utilized predict the future movement of the obstacles, tailgate an obstacle, and infer a lane line.

A lane management system for operating an automated vehicle includes a navigation-device, a vehicle-detector, and a controller suitable for use on a host-vehicle. The navigation-device is used to determine a preferred-route to a destination of the host-vehicle. The vehicle-detector is used to determine a relative-location of an other-vehicle proximate to the host-vehicle. The controller is in communication with the navigation-device and the vehicle-detector. The controller is configured to determine an alternate-route when the relative-location is such that a preferred-lane of the preferred-route is obstructed whereby the host-vehicle is unable to follow the preferred-route. Alternatively, the controller is configured to determine an initiate-time to perform a lane-change necessary to maneuver the host-vehicle into a preferred-lane of the preferred-route so the host-vehicle can follow the preferred-route, wherein the initiate-time is determined based on the relative-location.