The present disclosure provides autonomous vehicle systems and methods that include or otherwise leverage a motion planning system that generates constraints as part of determining a motion plan for an autonomous vehicle (AV). In particular, a scenario generator within a motion planning system can generate constraints based on where objects of interest are predicted to be relative to an autonomous vehicle. A constraint solver can identify navigation decisions for each of the constraints that provide a consistent solution across all constraints. The solution provided by the constraint solver can be in the form of a trajectory path determined relative to constraint areas for all objects of interest. The trajectory path represents a set of navigation decisions such that a navigation decision relative to one constraint doesn’t sacrifice an ability to satisfy a different navigation decision relative to one or more other constraints.

A vehicle behavior estimation method includes: detecting a speed of a first preceding vehicle traveling in front of a host vehicle in a first lane where the host vehicle is traveling; detecting a speed of an adjacent vehicle traveling in a second lane adjacent to the first lane; calculating a relative speed between the first preceding vehicle and the adjacent vehicle; predicting whether or not an absolute value of the relative speed will be at or below a speed threshold value within a predetermined time from a point time when a decrease in the absolute value of the relative speed starts to be detected; and estimating that the adjacent vehicle is likely to change lanes into the first lane when the absolute value of the relative speed is predicted to be at or below the speed threshold value within the predetermined time.

A system and method estimate a future path ahead of a current location of a vehicle. The system includes at least one processor programmed to: obtain an image of an environment ahead of a current arbitrary location of a vehicle navigating a road; obtain a trained system that was trained to estimate a future path on a first plurality of images of environments ahead of vehicles navigating roads; apply the trained system to the image of the environment ahead of the current arbitrary location of the vehicle; and provide, based on the application of the trained system to the image, an estimated future path of the vehicle ahead of the current arbitrary location.

An autonomous driving system acquires information concerning a vehicle density in an adjacent lane that is adjacent to a lane on which an own vehicle is traveling, when the own vehicle travels on a road having a plurality of lanes. The autonomous driving system selects the adjacent lane as an own vehicle travel lane, when the vehicle density in the adjacent lane that is calculated from the acquired information is lower than a threshold density that is determined in accordance with relations between the own vehicle and surrounding vehicles. The autonomous driving system performs lane change to the adjacent lane autonomously, or propose lane change to the adjacent lane to a driver, when the adjacent lane is selected as the own vehicle travel lane.

A trajectory setting device that sets a trajectory of a host vehicle includes a first path generation unit configured to generate a first path by assuming all obstacles around the host vehicle to be stationary obstacles, a second path generation unit configured to generate a second path when the moving obstacle is assumed to move independently, a third path generation unit configured to generate a third path when the moving obstacle is assumed to move while interacting with at least one of the other obstacles or the host vehicle, a reliability calculation unit configured to calculate reliability of the second path and reliability of the third path, and a trajectory setting unit configured to set the trajectory for traveling from the first path, the second path, and the third path based on the reliability of the second path and the reliability of the third path.

A vehicle control device includes an electronic control unit configured to: enlarge the detection range, when the electronic control unit determines that a current deceleration support control is control for passing the object; set a new target deceleration of the host vehicle when a new object with a possibility of collision with the host vehicle has been detected in the enlarged detection range; determine whether an interval from an ending time of the current deceleration support control to a starting time of the next deceleration support control is less than a threshold value, when the electronic control unit determines that the next deceleration support control is control for passing the new object; and perform one of the inter-vehicle distance control and acceleration support control from the ending time to the starting time, when the electronic control unit determines that the interval is less than the threshold value.

A method for supporting an automatically driving vehicle is provided. In one embodiment, it is ascertained whether it is possible for the automatically driving vehicle to change lanes to a fast lane in order to pass an obstacle located in front of the automatically driving vehicle on a roadway. If not, the automatically driving vehicle stops before reaching the obstacle and transmits a support request to a vehicle-external center. The vehicle-external center detects another vehicle in the surroundings of the automatically driving vehicle and instructs same to move in the direction of the automatically driving vehicle and change to the fast lane before reaching the automatically driving vehicle.

A vehicle control apparatus is mounted in a vehicle and includes: an object detecting unit that detects an object in a travelling direction of the vehicle; and a suppressing unit that suppresses driving force of the vehicle when the object detecting unit detects the object. The suppressing unit performs a first process to gradually increase the driving force when a command to move in the travelling direction is issued and the vehicle is stopped in a state in which the driving force of the vehicle is suppressed, and after the vehicle starts to move from the stopped state, performs a second process to gradually increase the driving force with an amount of increase per time in the driving force that is less than that in the first process.

Disclosed is a collision avoidance assisting apparatus which can execute an automatic braking process and an automatic steering process for avoiding collision with an obstacle. When the magnitude of a steering angle exceeds a predetermined threshold, the collision avoidance assisting apparatus determines that a driver has an intention of avoiding the collision by a steering operation and stops the automatic braking process and the automatic steering process. However, in such a case, the automatic braking process and the automatic steering process may be stopped when the steering angle exceeds the threshold as a result of execution of the automatic steering process. In view of this, when both the automatic braking process and the automatic steering process are being executed, the collision avoidance assisting apparatus continues the automatic braking process and the automatic steering process even when the magnitude of the steering angle is greater than the predetermined threshold.

Techniques are described for cognitive analysis for directed control transfer for autonomous vehicles. In-vehicle sensors are used to collect cognitive state data for an individual within a vehicle which has an autonomous mode of operation. The cognitive state data includes infrared, facial, audio, or biosensor data. One or more processors analyze the cognitive state data collected from the individual to produce cognitive state information. The cognitive state information includes a subset or summary of cognitive state data, or an analysis of the cognitive state data. The individual is scored based on the cognitive state information to produce a cognitive scoring metric. A state of operation is determined for the vehicle. A condition of the individual is evaluated based on the cognitive scoring metric. Control is transferred between the vehicle and the individual based on the state of operation of the vehicle and the condition of the individual.