Abstract: System, methods, and other embodiments described herein relate to a training system to train a driver about occurrences of anomalous driving events of automated vehicle systems. In one embodiment, a method includes determining, upon receiving a selection of a vehicle behavior from one or more anomalous driving events and a detected state change signal, whether the vehicle behavior affects one or more entities. The method includes assessing a state of the one or more entities to simulate the vehicle behavior according to a safety standard. The method includes triggering simulation of the vehicle behavior if the state satisfies a threshold. The method includes simulating the vehicle behavior by at least controlling the vehicle to simulate the vehicle behavior during automated driving of the vehicle.

Abstract: A method for controlling a driving condition component of a vehicle includes disabling automatic operation of the driving condition component based on enabling an autonomous operating mode of the vehicle. The method also includes determining, while the vehicle is operating in the autonomous mode, a current condition satisfies an unsafe driving condition for a manual operating mode of the vehicle. The method further includes enabling the driving condition component to mitigate based on predicting a human occupant will enable the manual operating mode during a time period associated with the current condition, the driving condition component being enabled prior to the human occupant switching from the autonomous operating mode to the manual operating mode.

Abstract: Systems and methods for selecting among different driving modes for autonomous driving of a vehicle may: generate output signals; determine the vehicle proximity information that indicates whether one or more vehicles are within the particular proximity of the vehicle; determine the internal passenger presence information that indicates whether one or more passengers are present in the vehicle; select a first driving mode or a second driving mode based on one or more determinations; and control the vehicle autonomously in accordance with the selection of either the first driving mode or the second driving mode.

Abstract: A method for controlling a sunshade of an autonomous vehicle is presented. The method includes determining that current conditions satisfy an activation condition. The method also includes predicting whether a driver will enable a manual mode during the current conditions. The method further includes activating a first sunshade in response to the satisfied activation condition regardless of whether the driver will enable the manual mode. The method still further includes activating a second sunshade in response to the satisfied activation condition when the driver will not enable the manual mode.

Abstract: A method for vehicle management performed by a vehicle control system includes monitoring a respective location associated with each vehicle of a plurality of vehicles based on location information transmitted from each of the plurality of vehicles. The method also includes receiving, from a user equipment (UE) associated with a customer, customer location information identifying a location of the customer. The method further includes receiving, from each vehicle of the plurality of vehicles, a set of current compartment climate values based on a triggering event at the vehicle. The method still further includes selecting one vehicle of the plurality of vehicles as a preferred vehicle based on a set of climate preferences associated with the customer. The method also includes transmitting, to the preferred vehicle, a message dispatching the preferred vehicle to the location of the customer, such that the preferred vehicle navigates to the location of the customer.

Abstract: A method for vehicle selection performed by a user equipment (UE) associated with a customer includes transmitting, from the UE to a vehicle control system, customer location information identifying a location of the customer. The method also includes receiving, from the vehicle control system based on transmitting the customer location information, vehicle location information and a set of compartment climate values associated with each vehicle of a set of vehicles within a range of the location of the customer. The method further includes outputting the current compartment climate value and a location associated with the vehicle location information of each vehicle of the set of vehicles for display at the UE.

Abstract: A method for route planning for an autonomous vehicle includes receiving a request to autonomously navigate to a destination. The method also includes identifying a stall factor on a route to the destination prior to departing to the destination, the stall factor delaying a time for arriving at the destination. The method further includes determining whether an occupant of the autonomous vehicle is capable of manually operating the autonomous vehicle in a manual operating mode or a semi-manual operating mode. The method still further includes identifying an alternate route to the destination based on identifying the stall factor and the occupant being incapable of manually operating the autonomous vehicle. The method also includes controlling the autonomous vehicle to drive on the alternate route instead of the route.

Abstract: A method for adjusting a seat in a vehicle includes adjusting the seat from an initial position to a passenger adjusted position based on receiving an input from a passenger. The method further includes determining the passenger exited the vehicle after adjusting the seat and predicting a likelihood of the passenger returning to the vehicle based on determining the passenger exited the vehicle. The method also includes adjusting the seat to the initial position based on the likelihood of the passenger returning is less than a passenger returning threshold. The method still further includes identifying a person approaching the vehicle after the passenger exited the vehicle based on information captured by one or more sensors of the vehicle. The method also includes adjusting the seat to a position associated with the previous passenger based on identifying the person approaching the vehicle as the previous passenger.

Abstract: A method for controlling a driving condition component of an autonomous vehicle is presented. The method includes determining whether current conditions would limit a driver's visibility. The method also includes predicting whether the driver will enable a manual mode during the current conditions. The method further includes controlling the driving condition component to mitigate the current conditions prior to the driver enabling the manual mode.

Abstract: A method for selecting an autonomous mobility-as-a-service (MAAS) vehicle is presented. The method includes transmitting, to a customer, location information of an autonomous MAAS vehicle within the customer's vicinity. The method also includes transmitting, to the customer, a compartment climate value corresponding to the autonomous MAAS vehicle. The method further includes selecting the autonomous MAAS vehicle based on the compartment climate value, such that the selected autonomous MAAS vehicle navigates to a location of the customer.

Abstract: In one embodiment, a method for collecting lane data is disclosed. The method includes collecting lane data, for a lane of a plurality of lanes of a road, from one or more sensors of a vehicle traveling in the lane. The method includes receiving an identifier of a lane of the plurality of lanes. The identified lane is a lane-of-interest and is different from the lane that the vehicle is traveling in. The method includes determining that the vehicle is not occupied. The method includes causing, in response to determining that the vehicle is not occupied, the vehicle to travel in the lane-of-interest. The method includes collecting lane data, for the lane-of-interest from the one or more sensors.

Abstract: In one embodiment, a method for collecting lane data is disclosed. The method includes collecting lane data for a first lane of a plurality of lanes of a road from one or more sensors of a vehicle traveling in the first lane. The method includes receiving an identifier of a lane-of-interest from the plurality of lanes. The method includes determining a probability that the vehicle will receive a ride request while traveling in the first lane. The method includes determining that the probability satisfies a threshold. The method includes, in response to determining that the probability satisfies the threshold, causing the vehicle to travel in the lane-of-interest. The method includes collecting lane data for the lane-of-interest from the one or more sensors.

Abstract: A method for route planning for an autonomous vehicle includes determining whether a route to a destination includes a stall factor and determining whether the autonomous vehicle will be occupied during the route. The method also includes determining an alternate route based on the determined stall factor when the autonomous vehicle will be unoccupied. The method still further includes controlling the autonomous vehicle to drive on the alternate route when the alternate route does not include a stall factor.

Abstract: A method for adjusting a seat in a vehicle is presented. The method includes receiving a user input from a passenger to adjust the seat from a default position to a passenger adjusted position. The method also includes predicting, in response to the passenger exiting the vehicle, a likelihood of the passenger returning to the vehicle. The method still further includes maintaining the passenger adjusted position when the likelihood of the passenger returning to the vehicle is greater than a threshold.

Abstract: Systems, methods, and other embodiments described herein relate to processing an image having a distortion and an object. The method according to some embodiments includes acquiring an image having a distortion and performing a first object detection analysis on a first portion of the image. Next, the method includes performing an undistortion process on the image, and after, performing a second object detection analysis on a second portion of the image. The second portion of the image is different than the first portion of the image. The method can then combine a first result of the first object detection analysis with a second result of the second object detection analysis.

Abstract: In step S11, a color image CIMG is acquired. In step S12, a distance image DIMG is acquired. In step S13, the color image CIMG is projected onto the distance image DIMG. An alignment of the color image CIMG and the distance image DIMG is performed prior to the projection of the color image CIMG. In step S14, it is determined whether or not a basic condition is satisfied. In S15, it is determined whether or not a special condition is satisfied. If a judgement result of the steps S14 or S15 is positive, then in step S16 a first data point and a second data point on the distance image DIMG are associated. If bot of the judgement results of steps S14 and S15 are negative, data points are not associated in step S17.

Abstract: Systems, methods, and other embodiments described herein relate to a method of determining stereo depth of an object. One method includes obtaining an image captured with a stereo camera arrangement. The stereo camera arrangement can be installed in one of a vehicle and a robotic apparatus, for example. The image captures a portion of the environment associated with the stereo camera arrangement. The method can further include identifying an object in the image, determining an object class for the object, determining a size parameter of the object, determining a size parameter of the object class, determining a maximum disparity for the object with the size parameter of the object and the size parameter of the object class, and determining a stereo depth of the object based on the maximum disparity.

Abstract: An object recognition apparatus include a storage device and a processor. The storage device stores peripheral information and tolerance information. The tolerance information is information in which the degree of tolerance for the undetected object is represented for each class of the object. The peripheral information is acquired by a sensor device provided in the vehicle. The processor performs object recognition process for recognizing an object around the vehicle. In the object recognition process, the processor identifies the object and its class to be detected based on the peripheral information, and calculates the likelihood that is a parameter representing the probability of detection of the object. Further, the processor calculates a likelihood threshold corresponding to the object based on the tolerance information, and determines whether to output the identification result of the object based on the comparative between the likelihood and the likelihood threshold.

Abstract: In one embodiment, a system for correcting parallax error is provided. An image is received from a camera and a plurality of points is received from a LiDAR sensor. The points are placed on the image based on coordinates associated with each point. The image is divided into a plurality of cells by placing a grid over the image. For each cell, a minimum distance between the points in the cell and the camera is determined. For each cell, a margin is calculated based on the determined minimum distance. For each cell, points that have a distance from the camera that is greater than the minimum distance plus the margin are removed or deleted. The image and/or the remaining points are then used to provide one or more vehicle functions.

Abstract: A method for controlling an operating mode of a vehicle is presented. The method includes determining a current range of the vehicle while the vehicle is operating in a first operating mode. The method also includes determining a distance to a destination. The method further includes controlling the vehicle to operate in a second operating mode instead of the first operating mode when the range is less than the distance to the destination.