In one embodiment, a cognitive load driving assistant increases driving safety based on cognitive loads. In operation, the cognitive load driving assistant computes a current cognitive load of a driver based on sensor data. If the current cognitive load exceeds a threshold cognitive load, then the cognitive load driving assistant modifies the driving environment to reduce the cognitive load required to perform the primary driving task and/secondary task(s), such as texting via a cellular phone. The cognitive load driving assistant may modify the driving environment indirectly via sensory feedback to the driver or directly through reducing the complexity of the primary driving task and/or secondary tasks. In particular, if the driver is exhibiting elevated cognitive loads typically associated with distracted driving, then the cognitive load driving assistant modifies the driving environment to allow the driver to devote appropriate mental resources to the primary driving task, thereby increasing driving safety.

Systems, methods, and computer-readable media are disclosed for improved smart infrastructure data transfer. An example method may involve receiving, by a smart infrastructure device and from a first vehicle, first information associated with the first vehicle in a first format associated with a first communication protocol. The first information is converted from the first format into an agnostic format. An image, video, or real-time feed of an environment of the smart infrastructure device is captured. The first vehicle and a second vehicle in the image, video, or real-time feed is identified. It is determined that the second vehicle is temporarily or permanently incapable of performing a communication with the smart infrastructure device based on the image, video, or real-time feed. The image, video, or real-time feed is analyzed to generate second information associated with the second vehicle. The second information is converted into the agnostic format.

Systems and methods are provided for navigating a host vehicle. In an embodiment, a processing device may be configured to receive images captured over a time period; analyze images to identify a target vehicle; receive map information associated including a plurality of target trajectories; determine, based on analysis of the images, first and second estimated positions of the target vehicle within the time period; determine, based on the first and second estimated positions, a trajectory of the target vehicle over the time period; compare the determined trajectory to the plurality of target trajectories to identify a target trajectory traversed by the target vehicle; determine, based on the identified target trajectory, a position of the target vehicle; and determine a navigational action for the host vehicle based on the determined position.

An information processing device estimates a road shape of a road on which a host vehicle travels based on map information acquired from a map information unit and sensor information acquired from a sensor unit. The information processing device includes a sensor resolution characteristic management unit which manages a sensor resolution characteristic of the sensor unit, which corresponds to a distance from a host vehicle position of the host vehicle to a position where the intersection shape changes, an appearance prediction unit which predicts an appearance of the road corresponding to the distance based on the sensor resolution characteristic acquired from the sensor resolution characteristic management unit, and a model generation unit which generates a road model obtained by modeling the road based on the appearance of the road predicted by the appearance prediction unit.

A method for operating a vehicle includes carrying out a lane-keeping control of the vehicle along a course of a lane travelled in by the vehicle. When lane markings are detected, the course of the lane is determined on a basis of detected lane markings. When lane markings are not detected, the course of the lane is determined in a mapped-based manner on a basis of data from a digital map where a rough localization of the vehicle in the digital map and a fine localization of the vehicle in the digital map is performed.

An information processing apparatus according to an embodiment of the present technology includes a first acquisition section, a setting section, a storage, and a display controller. The first acquisition section acquires map information. On the basis of designation of a region that is performed by a user, the setting section sets a content region with which content data is associated. The storage stores therein the set content region and the map information in a state of being associated with each other. The display controller causes the content region to be superimposed on the map information to be displayed. This makes it possible to easily create content that uses position information.

The present disclosure relates to navigation and to systems and methods for using a dual sensor readout channel to allow for frequency detection. In one implementation, at least one processing device may receive a plurality of images acquired by a camera onboard a host vehicle, wherein the plurality of images are received via a first channel and via a second channel, and wherein the first channel is associated with a first frame capture rate, and the second channel is associated with a second frame capture rate different from the first frame capture rate. The processing device may use images received via the first channel to detect flickering and non-flickering light sources in an environment of the host vehicle; and provide, based on images received via the second channel, images for showing on one or more human-viewable displays.

A method for improving performance of a local device based on guide data from a remote device, according to one embodiment of the present disclosure, includes transmitting, to the remote device, first image data generated by the local device at a first time point, receiving guide data related to the first image data from the remote device, and registering, by a processor, the guide data to second image data generated by the local device at a second time point, based on first spatial information on the first image data, wherein the second time point is a time point that is after the first time point. A trained model for object recognition according to the present disclosure may include a deep neural network generated through machine learning, and the transmitting of the guide data may be performed in an Internet of Things (IoT) environment using a 5G network.

Systems and methods for perceiving a field around a mobile device include a sensor system has a distance sensor arranged on a mobile device. The sensor system captures distance measurements of a field of view. The distance measurements are captured at a unique set of angular scan positions per revolution of the distance sensor over a sequence of scan rotations. A perception system generates a three-dimensional point cloud representation of the field of view based on the distance measurements for each scan rotation in the sequence of scan rotations. The perception system generates a composite three-dimensional depth map of the field of view by compiling each of the three-dimensional point cloud representations for the sequence of scan rotations. Each of the three-dimensional point cloud representations has a resolution that is lower than a resolution of the composite three-dimensional depth map of the field of view.

A method for determining a geographical location of a vehicle (10) includes using a camera/sensor device (20) of the vehicle for recording (S10) first image and sensor data (30) from surroundings of the vehicle (10) while the vehicle (10) is traveling a route. The first image and sensor data (30) are assigned geographical coordinates and are sent to a data evaluation unit (50) for creating a digital map. The method continues by using a second camera and sensor device (20) for recording (S40) second image and sensor data (30) from surroundings while the vehicle (10) is traveling the same route and sending (S50) the recorded second image and sensor data (30) to the data evaluation unit (50). The data evaluation unit (50) compares (S60) the recorded second image and sensor data (30) with the digital map of the surroundings (70) and determines (S70) a position of the vehicle (10).