An information processing apparatus capable of communicating with a vehicle includes a storage unit configured to store a location of an autonomous driving prohibition section on a map in association with a release condition set based on a traveling state of the vehicle; and a control unit configured to acquire the traveling state of the vehicle including the location of the vehicle on the map from the vehicle through communication, the control unit being configured to determine whether or not to release the autonomous driving prohibition section based on the acquired traveling state of the vehicle, the location of the autonomous driving prohibition section and the release condition of the autonomous driving prohibition section, the location and the release condition being stored in the storage unit.

An information processing apparatus capable of communicating with a plurality of autonomous driving vehicles includes: a storage unit configured to store a location of an autonomous driving difficult section on a map in association with a predetermined release condition; and a control unit configured to acquire from the plurality of autonomous driving vehicles reporting data of each of the autonomous driving vehicles through communication, the reporting data including a location of the vehicle on the map and at least one of reliability of the autonomous driving, difficulty state information indicative of the autonomous driving being difficult or not difficult, and a result of driver intervention, and the control unit being configured to determine whether to release the autonomous driving difficult section based on the acquired reporting data and on the location and the release condition of the autonomous driving difficult section stored in the storage unit.

Embodiments of the invention provide a control system for a hybrid electric vehicle, the vehicle having a powertrain comprising at least one electric propulsion motor and at least one engine, the control system being operable to control the vehicle to operate in an electric vehicle (EV) mode in which the at least one engine remains switched off and the at least one electric propulsion motor is configured to deliver drive torque and a boost mode in which the at least one engine is switched on to provide additional power to drive the vehicle. When the vehicle is operating in EV mode the system is further operable to determine whether a boost location exists ahead of the vehicle being a location at which a gradient of a driving surface is sufficiently high to require selection of the boost mode, the control system being operable automatically to command starting of the at least one engine before the boost location is reached.

Systems and methods are provided for distributing a crowdsourced sparse map for autonomous vehicle navigation. In one implementation, a method of generating a road navigation model for use in autonomous vehicle navigation may include receiving navigation information associated with a common road segment from a plurality of vehicles; storing the navigation information associated with the common road segment; generating at least a portion of an autonomous vehicle road navigation model for the common road segment based on the navigation information; and distributing the autonomous vehicle road navigation model to one or more autonomous vehicles for use in autonomously navigating along the common road segment. The autonomous vehicle road navigation model may include at least one line representation of a road surface feature extending along the common road segment, and each line representation may representing a path along the common road segment substantially corresponding with the road surface feature.

Systems and methods are provided for aligning crowdsourced map data. In one implementation, a method of determining a line representation of a road surface feature extending along a road segment may include receiving a first set of drive data including position information associated with the road surface feature, receiving a second set of drive data including position information associated with the road surface feature, segmenting the first set of drive data into first drive patches and segmenting the second set of drive data into second drive patches, longitudinally aligning the first set of drive data with the second set of drive data within corresponding patches, and determining the line representation of the road surface feature based on the longitudinally aligned first and second drive data in the first and second draft patches.

Systems and methods are provided for navigating a vehicle using a crowdsourced sparse map. In one implementation, a method of autonomously navigating a vehicle along a road segment may include receiving a sparse map model, receiving at least one image representative of an environment of the vehicle, analyzing the sparse map model and the at least one image, and determining an autonomous navigational response for the vehicle based on the analysis of the sparse map model and the at least one image. The at least one image may be received from a camera, and the sparse map model may include at least one line representation of a road surface feature extending along the road segment, each line representation representing a path along the road segment substantially corresponding with the road surface feature.

Systems and methods are provided for crowdsourcing a sparse map for autonomous vehicle navigation. In one implementation, a non-transitory computer-readable medium may include a sparse map for autonomous vehicle navigation along a road segment. The sparse map may include at least one line representation of a road surface feature extending along the road segment, each line representation representing a path along the road segment substantially corresponding with the road surface feature, and wherein the road surface feature is identified through image analysis of a plurality of images acquired as one or more vehicles traverse the road segment and a plurality of landmarks associated with the road segment.

Systems and methods are provided for crowdsourcing road surface information collection. In one implementation, a method of collecting road surface information for a road segment may include receiving at least one image representative of a portion of the road segment, identifying in the at least one image at least one road surface feature along the portion of the road segment, determining a plurality of locations associated with the road surface feature according to a local coordinate system of the vehicle, and transmitting the determined plurality of locations from the vehicle to a server. The determined locations may be configured to enable determination by the server of a line representation of the road surface feature extending along the road segment.

The topographic environment detection method and a topographic environment detection system based on a binocular stereo camera, and an intelligent terminal are provided. The topographic environment detection method includes: obtaining a left-eye image and a right-eye image about a same road scenario, and processing the left-eye image and the right-eye image to obtain a dense disparity map of the road scenario; converting image information in a detection region into 3D point cloud information in a world coordinate system in accordance with the dense disparity map; fitting a road surface model in accordance with the 3D point cloud information; inputting an image in the detection region into a trained semantic segmentation model, and obtaining a segmentation result from the semantic segmentation model; and obtaining topographic information about the detection region in accordance with the segmentation result, and transmitting the topographic information to a vehicle assistant driving system.

A computer system comprising processing circuitry is described. The processing circuitry is configured to obtain topography data of a current road segment and an upcoming road segment and to determine a topography change between the current road segment and the upcoming road segment. The processing circuitry is further configured to determine a maximum propulsion torque based on the topography change and to limit a torque indicatable by an operator-controlled input of a vehicle based on the maximum propulsion torque.