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.

The invention relates to a method for assisting in the driving of a motor vehicle on a fast road with carriageways separated by a safety rail in which the presence of the safety rail is detected (40) and the safety rail is modelled from measurements performed continuously by at least one laser scanner sensor (20) mounted on the motor vehicle, with the determination of a confidence index I.sub.CONF associated with the detection by the laser scanner sensor (20); an automatic driving mode is activated (60) if the confidence index I.sub.CONF is above a confidence threshold I.sub.th; this mode is maintained (120) as long as a current confidence index associated with the detection is above the confidence threshold I.sub.th; and this mode is deactivated if the current confidence index passes below said confidence threshold; according to the invention, the density of traffic in front of the motor vehicle is estimated (70) from images captured by an embedded camera (30), so that, after activation (60) of the automatic driving mode, said current confidence index taken into account for the deactivation and the maintaining of the mode is a function of a combination of the confidence index I.sub.CONF associated with the detection of presence of the safety rail and of the estimated traffic density.

The present disclosure relates to systems and methods for host vehicle navigation. In one implementation, a navigation system for a host vehicle may include at least one processing device programmed to receive, from a camera, a plurality of images representative of an environment of the host vehicle; analyze the plurality of images to identify a first flow of traffic and a second flow of traffic; determine a presence of at least one navigational state characteristic indicative of an alternating merging of the first flow of traffic and the second flow of traffic into a merged lane; cause at least a first navigational change to allow one target vehicle from the first flow of traffic to proceed ahead of the host vehicle; and cause at least a second navigational change to cause the host vehicle to follow the target vehicle into the merged lane.

The present disclosure may be directed to a computer-assisted or autonomous driving (CA/AD) vehicle that receives a plurality of indications of a condition of one or more features of a plurality of locations of a roadway, respectively, encoded in a plurality of navigation signals broadcast by a plurality of transmitters as the CA/AD vehicle drives past the locations enroute to a destination. The CA/AD vehicle may then determine, based in part on the received indications, driving adjustments to be made and send indications of the driving adjustments to a driving control unit of the CA/AD vehicle.

A pedestrian crosswalk through which a subject vehicle is expected to pass is specified as a target pedestrian crosswalk. Road configurations close to the target pedestrian crosswalk are detected. Traffic lines of moving objects crossing the target pedestrian crosswalk are estimated on the basis of the road configurations. An area including the estimated traffic lines is set as a detection area of a detector detecting objects around the subject vehicle. The moving objects are detected by the detector in the detection area. Travel of the subject vehicle is controlled on the basis of a detection result of the detector.

A method for operating a vehicle, wherein a check is carried out to determine whether a switch of the driving mode can be safely carried out and a corresponding output is provided, wherein an estimation is made as to whether the road class traversed by the vehicle corresponds to a target road class, including the steps of: ascertaining the position of the vehicle using a satellite navigation system, comparing the ascertained position with a road map, wherein the road map includes an allocation of roads according to road classes, and determining whether a road of the target road class is located in a specified area around the ascertained position on the road map.

Drive envelope determination is described. In an example, a vehicle can capture sensor data while traversing an environment and can provide the sensor data to computing system(s). The sensor data can indicate agent(s) in the environment and the computing system(s) can determine, based on the sensor data, a planned path through the environment relative to the agent(s). The computing system(s) can also determine lateral distance(s) to the agent(s) from the planned path. In an example, the computing system(s) can determine modified distance(s) based at least in part on the lateral distance(s) and information about the agents. The computing system can determine a drive envelope based on the modified distance(s) and can determine a trajectory in the drive envelope.

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.

A travel-lane determination unit determines whether a vehicle has entered a traffic lane on which a toll booth is provided. After it is determined that the vehicle has entered the traffic lane, a travel control unit pulls the vehicle to a position shifted from the center of the traffic lane toward the toll booth in a traffic-lane width direction, by the time when the vehicle reaches the toll booth.

A traveling position of a host vehicle is controlled using barrier lines as a reference. Autonomous driving is executed by either a both-side recognition control in which the position of the host vehicle is controlled based on left and right barrier lines, or a one-side recognition control in which the position of the host vehicle is controlled based on either one of the left or right the barrier lines. A region currently being traveled in is stored as a steering-wheel-turned region when a steering wheel is turned in one direction and subsequently turned in a returning direction due to the switching of control between the both-side recognition control and the one-side recognition control, and autonomous driving under the control preceding the steering-wheel-turned region is continued when the steering-wheel-turned region is subsequently traveled in.