This disclosure describes methods and techniques for estimating lanes for a vehicle. The methods and techniques include determining a first preliminary estimate of lanes based on a plurality of lane markings at a location of the vehicle, determining a second preliminary estimate of lanes based on a plurality of trails of objects at the location of the vehicle, comparing the first preliminary estimate of lanes and the second preliminary estimate of lanes, and determining a final estimate of lanes at the location of the vehicle based on the comparing.

A driving risk assessment and control decision-making method for an autonomous vehicle includes: detecting the surrounding state of the vehicle multiple times to generate multiple sensing signals; quantifying the sensing signals to generate multiple sensing values and calculating a sensing average value of the sensing values; calculating a sensing error value between each sensing value and the sensing average value, a sensing error average value of sensing error values and a sensing error variation value; integrating the sensing error average value, the sensing error variation value and a sensor systematic error average value and a sensor systematic error variation value to generate a sensing signal correction value; combining the sensing values and the sensing signal correction value to generate multiple sensing signal reference values; judging whether a stability of the sensing signal reference values falls within a preset range; generating a control mechanism based on the judgement.

A driving assistance system includes an assistance information acquisition unit configured to acquire assistance information from an information processing server, a setting information acquisition unit configured to acquire an assistance timing of an in-vehicle driving assistance unit set by a driver of a target vehicle, and an assistance information providing unit configured to execute driving assistance for the target vehicle based on the acquired assistance information. The assistance information providing unit provides the driving assistance based on the assistance information when the assistance timing is within a first timing range and does not execute the driving assistance based on the assistance information when the assistance timing is within a second timing range later than the first timing range.

A system comprising a computer including instructions to collect object data about each of a plurality of objects within a zone from sensors on a roadside infrastructure unit. The zone includes a road intersection. The object data includes an object type, an object location, an object speed and an object direction of travel for each object. The computer further includes instructions to determine, based on the object data, an availability level for execution of one or more paths through the road intersection by a vehicle wherein the availability level for each of the one or more paths is based on a likelihood of interference between any of the objects and the vehicle as the vehicle traverses the respective path. The computer further includes instructions to transmit an availability message to the vehicle including the availability level for each of the one or more paths.

Aspects of the disclosure provide for controlling an autonomous vehicle. For instance, a trajectory for the autonomous vehicle to traverse in order to follow a route to a destination may be generated. A first error value for a boundary of an object, a second error value for a location of the autonomous vehicle, a third error value for a predicted future location of the object may be received. An uncertainty value for the object may be determined by combining the first error value, the second error value, and the third error value. A lateral gap threshold for the object may be determined based on the uncertainty value. The autonomous vehicle may be controlled in an autonomous driving mode based on the lateral gap threshold for the object.

The techniques discussed herein may comprise an autonomous vehicle guidance system that generates a path for controlling an autonomous vehicle based at least in part on a static object map and/or one or more dynamic object maps. The guidance system may identify a path based at least in part on determining set of nodes and a cost map associated with the static and/or dynamic object, among other costs, pruning the set of nodes, and creating further nodes from the remaining nodes until a computational or other limit is reached. The path output by the techniques may be associated with a cheapest node of the sets of nodes that were generated.

Provided are an automatic parking control method and apparatus, relating to the industrial field of vehicles. The method comprises: when a speed of a vehicle is in a preset speed range and a time that the speed of the vehicle is in the preset speed range is greater than or equal to a preset time, collecting image data and radar data around the vehicle; inputting the image data and the radar data into a preset convolutional neural network model, and outputting at least one parking slot information; selecting target parking slot information according to a received parking-in selection operation; and according to the target parking slot information, generating a vehicle parking-in track for the vehicle to automatically park according to the vehicle parking-in track.

An automatic valet parking system includes a vehicle, a parking server, and a map server. The parking server includes a server-side operation planning portion that generates a server-side operation plan including a route for guiding the vehicle to a target position. The vehicle includes: an operation plan determination portion that determines whether the server-side operation plan is false; an automatic operation control portion that performs automatic operation control according to the server-side operation plan; and an information transmission portion that acquires vehicle-related information about the vehicle and to transmit the vehicle-related information to the parking server when the operation plan determination portion determines that the server-side operation plan is false.

In an overhead-structure recognition device to be mounted to a vehicle, a determination unit is configured to, in response to a vertical distance between an object of interest and a high-reflectivity object being greater than or equal to a predefined value of vertical distance, determine that the object of interest is an overhead structure which is a structure located above the vehicle that does not obstruct travel of the vehicle. The object of interest corresponds to a subset of interest among a plurality of subsets acquired by dividing range point cloud data. The high-reflectivity object is an object other than the object of interest, among objects corresponding to the respective subgroups, whose reflectance is greater than or equal to a predefined value of reflectance.

A controller may be configured to receive one or more measured rotational speeds of a wheel of a vehicle. The controller may be configured to determine whether the one or more measured rotational speeds are unreliable relative to one or more previous rotational speeds of the wheel of the vehicle. The controller may be configured to calculate a replacement rotational speed of the wheel and use the replacement rotational speed of the wheel to control or restrict movement of the vehicle using or based on the replacement rotational speed in place of the one or more measured rotational speeds.