Abstract: A route planner detects a pedestrian from surrounding data representing a situation of surroundings of a vehicle, identifies a position of a pause-by object in the surroundings of the vehicle prompting the pedestrian to pause, creating a travel route over which the vehicle will travel when the pedestrian is detected, assuming the pedestrian will enter a road on which the vehicle will travel when the identified position of the pause-by object is not in the vicinity of the pedestrian, and assuming the pedestrian will not enter the road when the identified position of the pause-by object is in the vicinity of the pedestrian.

Abstract: A motion prediction device detecting, from surrounding data representing a situation in a predetermined range of surroundings of a vehicle, a traffic participant existing in the predetermined range, determining whether there is a blind area not represented in the surrounding data in the predetermined range, when it is determined that there is a blind area presuming that there is a virtual traffic participant in the blind area, and predicting motion of the traffic participant caused by the presence of the virtual traffic participant.

Abstract: The driver assistance system of the present disclosure is configured to execute processing comprising following four processes. The first process is to extract risk target information related to a risk target that causes a collision risk to a vehicle from information related to a peripheral situation of the vehicle. The second process is to obtain natural phenomenon information related to a natural phenomenon that affects the risk target. The third process is to determine a risk parameter that quantifies the collision risk based on the risk target information and the natural phenomenon information. The fourth process is to determine a manipulated variable of an actuator for controlling movement of the vehicle to reduce the collision risk based on the risk parameter.

Abstract: In one embodiment, a signal light state detection system includes one or more processors, an a non-transitory memory module storing computer-readable instructions. The computer-readable instructions are configured to cause the one or more processors to receive a first image of a vehicle and receiving a second image of the vehicle, wherein the second image is later in time from the first image, and generate a warped image from the first image and the second image, wherein the warped image has individual pixels of one of the first image and the second image that are shifted to locations of corresponding pixels of the other of the first image and the second image. The one or more processors further generate a difference image from the warped image and one of the first image and the second image, and determine, using a classifier module, a probability of a state of vehicle signal lights.

Abstract: A behavior prediction device comprising: a moving object behavior detection unit configured to detect moving object behavior, a behavior prediction model database that stores a behavior prediction model, a behavior prediction calculation unit configured to calculate a behavior prediction of the moving object using the behavior prediction model, a prediction deviation determination unit configured to determine whether a prediction deviation occurs based on the behavior prediction and a detection result of the moving object behavior corresponding to the behavior prediction, a deviation occurrence reason estimation unit configured to estimate a deviation occurrence reason when determination is made that the prediction deviation occurs, and an update necessity determination unit configured to determine a necessity of an update of the behavior prediction model database based on the deviation occurrence reason when the determination is made that the prediction deviation occurs.

Abstract: System, methods, and other embodiments described herein relate to identifying rear indicators of a nearby vehicle. In one embodiment, a method includes, in response to detecting a nearby vehicle, capturing signal images of a rear portion of the nearby vehicle. The method includes computing a braking state for brake lights of the nearby vehicle that indicates whether the brake lights are presently active by analyzing the signal images according to a brake classifier. The method includes computing a turn state for rear turn signals of the nearby vehicle that indicates which of the turn signals are presently active by analyzing regions of interest from the signal images according to a turn classifier. The brake classifier and the turn classifier are comprised of a convolutional neural network and a long short-term memory recurrent neural network (LSTM-RNN). The method includes providing electronic outputs identifying the braking state and the turn state.

Abstract: Arrangements related to operating an autonomous vehicle in view-obstructed environments are described. At least a portion of an external environment of the autonomous vehicle can be sensed to detect one or more objects located therein. An occupant viewable area of the external environment can be determined. It can be determined whether one or more of the detected one or more objects is located outside of the determined occupant viewable area. Responsive to determining that a detected object is located outside of the determined occupant viewable area, one or more actions can be taken. For instance, the action can include presenting an alert within the autonomous vehicle. Alternatively or in addition, the action can include causing a current driving action of the autonomous vehicle to be modified.

Abstract: A behavior prediction device comprising: a moving object behavior detection unit configured to detect moving object behavior, a behavior prediction model database that stores a behavior prediction model, a behavior prediction calculation unit configured to calculate a behavior prediction of the moving object using the behavior prediction model, a prediction deviation determination unit configured to determine whether a prediction deviation occurs based on the behavior prediction and a detection result of the moving object behavior corresponding to the behavior prediction, a deviation occurrence reason estimation unit configured to estimate a deviation occurrence reason when determination is made that the prediction deviation occurs, and an update necessity determination unit configured to determine a necessity of an update of the behavior prediction model database based on the deviation occurrence reason when the determination is made that the prediction deviation occurs.

Abstract: System, methods, and other embodiments described herein relate to identifying rear indicators of a nearby vehicle. In one embodiment, a method includes, in response to detecting a nearby vehicle, capturing signal images of a rear portion of the nearby vehicle. The method includes computing a braking state for brake lights of the nearby vehicle that indicates whether the brake lights are presently active by analyzing the signal images according to a brake classifier. The method includes computing a turn state for rear turn signals of the nearby vehicle that indicates which of the turn signals are presently active by analyzing regions of interest from the signal images according to a turn classifier. The brake classifier and the turn classifier are comprised of a convolutional neural network and a long short-term memory recurrent neural network (LSTM-RNN). The method includes providing electronic outputs identifying the braking state and the turn state.

Abstract: Minimizing incorrect associations of sensor data for an autonomous vehicle are described. A driving environment of the autonomous vehicle includes a stationary object and a dynamic object. Such objects can be detected by radar sensors and/or lidar sensors. In one example, a history of radar observation can be used to minimize incorrect sensor data associations. In such case, the location of a stationary object in the driving environment can be determined. When a dynamic object passes by the stationary object, lidar data of the dynamic object is prevented from being associated with radar data obtained substantially at the determined location of the stationary object. In another example, identifiers assigned to radar data can be used to minimize incorrect sensor data associations. In such case, lidar data of an object can be associated with radar data having a particular identifier.

Abstract: System, methods, and other embodiments described herein relate to autonomously controlling a vehicle according to a trajectory plan. In one embodiment, a method includes updating, upon traveling over at least a portion of a current segment of a roadway, the trajectory plan for a subsequent segment of the roadway by setting a fixed portion of the trajectory plan to include: (i) a steering parameter to be fixed for a first duration of time and (ii) a speed parameter to be fixed for a second duration of time. The first duration of time and the second duration of time are of different lengths. The method includes computing input controls for autonomously controlling the vehicle according to the fixed portion of the trajectory plan. The method includes controlling the vehicle according to the input controls over the subsequent segment of the roadway.

Abstract: System, methods, and other embodiments described herein relate to autonomously controlling a vehicle according to a trajectory plan. In one embodiment, a method includes updating, upon traveling over at least a portion of a current segment of a roadway, the trajectory plan for a subsequent segment of the roadway by setting a fixed portion of the trajectory plan to include: (i) a steering parameter to be fixed for a first duration of time and (ii) a speed parameter to be fixed for a second duration of time. The first duration of time and the second duration of time are of different lengths. The method includes computing input controls for autonomously controlling the vehicle according to the fixed portion of the trajectory plan. The method includes controlling the vehicle according to the input controls over the subsequent segment of the roadway.

Abstract: Arrangements related to operating an autonomous vehicle in view-obstructed environments are described. At least a portion of an external environment of the autonomous vehicle can be sensed to detect one or more objects located therein. An occupant viewable area of the external environment can be determined. It can be determined whether one or more of the detected one or more objects is located outside of the determined occupant viewable area. Responsive to determining that a detected object is located outside of the determined occupant viewable area, one or more actions can be taken. For instance, the action can include presenting an alert within the autonomous vehicle. Alternatively or in addition, the action can include causing a current driving action of the autonomous vehicle to be modified.

Abstract: Arrangements related to operating an autonomous vehicle in view-obstructed environments are described. At least a portion of an external environment of the autonomous vehicle can be sensed to detect one or more objects located therein. An occupant viewable area of the external environment can be determined. It can be determined whether one or more of the detected one or more objects is located outside of the determined occupant viewable area. Responsive to determining that a detected object is located outside of the determined occupant viewable area, one or more actions can be taken. For instance, the action can include presenting an alert within the autonomous vehicle. Alternatively or in addition, the action can include causing a current driving action of the autonomous vehicle to be modified.

Abstract: Systems and methods relating to the operation of an autonomous vehicle relative to forward objects in an external environment are described. At least a forward portion of the external environment can be sensed to detect an object therein. A size adjustment factor can be determined to predict a laterally innermost point of the detected object relative to the autonomous vehicle. A driving maneuver for the autonomous vehicle can be determined based at least partially on the predicted laterally innermost point of the detected object relative to the autonomous vehicle.

Abstract: Minimizing incorrect associations of sensor data for an autonomous vehicle are described. A driving environment of the autonomous vehicle includes a stationary object and a dynamic object. Such objects can be detected by radar sensors and/or lidar sensors. In one example, a history of radar observation can be used to minimize incorrect sensor data associations. In such case, the location of a stationary object in the driving environment can be determined. When a dynamic object passes by the stationary object, lidar data of the dynamic object is prevented from being associated with radar data obtained substantially at the determined location of the stationary object. In another example, identifiers assigned to radar data can be used to minimize incorrect sensor data associations. In such case, lidar data of an object can be associated with radar data having a particular identifier.

Abstract: Systems and methods relating to the operation of an autonomous vehicle relative to forward objects in an external environment are described. At least a forward portion of the external environment can be sensed to detect an object therein. A size adjustment factor can be determined to predict a laterally innermost point of the detected object relative to the autonomous vehicle. A driving maneuver for the autonomous vehicle can be determined based at least partially on the predicted laterally innermost point of the detected object relative to the autonomous vehicle.

Abstract: Various manners of changing travel lanes for an autonomous vehicle are described. A target gap between a pair of neighboring vehicles located in a travel lane that is adjacent to the current travel lane of the autonomous vehicle can be identified. It can be determined whether the size of the first target gap is acceptable or unacceptable. Responsive to determining that the size of the first target gap is unacceptable, an alternative lane changing maneuver for the autonomous vehicle can be determined. The autonomous vehicle can be caused to implement the alternative lane changing maneuver.

Abstract: An automated driving system and methods are disclosed. The automated driving system includes a perception system associated with an autonomous vehicle. Sensors in communication with the perception system can detect an object of interest. Based on information specific to the environment surrounding the autonomous vehicle, the automated driving system can determine a vehicle path proximate to the object of interest. Based on properties of the object of interest, the automated driving system can determine a preferred gap between the vehicle path and the object of interest. The automated driving system can also determine an actual gap between the vehicle path and the object of interest. Based on the difference between the preferred gap and the actual gap, the automated driving system can determine a speed profile for the autonomous vehicle along the vehicle path and control the autonomous vehicle to follow the vehicle path according to the speed profile.

Abstract: Arrangements related to operating an autonomous vehicle in view-obstructed environments are described. At least a portion of an external environment of the autonomous vehicle can be sensed to detect one or more objects located therein. An occupant viewable area of the external environment can be determined. It can be determined whether one or more of the detected one or more objects is located outside of the determined occupant viewable area. Responsive to determining that a detected object is located outside of the determined occupant viewable area, one or more actions can be taken. For instance, the action can include presenting an alert within the autonomous vehicle. Alternatively or in addition, the action can include causing a current driving action of the autonomous vehicle to be modified.