Methods and systems for controlling navigation of an autonomous vehicle for traversing a drivable area are disclosed. The methods include receiving information relating to a drivable area that includes a plurality of polygons, identifying a plurality of logical edges that form a boundary of the drivable area, sequentially and repeatedly analyzing concavities of each the plurality of logical edges until identification of a first logical edge that has a concavity greater than a threshold, creating a first logical segment of the boundary of the drivable area. This segmentation may be repeated until each of the plurality of logical edges has been classified. The method may include creating and adding (to a map) a data representation of the drivable area that comprises an indication of the plurality of logical segments, and adding the data representation to a road network map comprising the drivable area.

A prediction apparatus comprising acquisition means for acquiring information of another vehicle existing on the periphery of a self-vehicle and information of an object existing on the periphery of the other vehicle, and prediction means for predicting a behavior of the other vehicle based on the information of the other vehicle and the information of the object acquired by the acquisition means.

Systems of an electrical vehicle and the operations thereof are provided that use object profiles to select autonomous vehicle operations, including acceleration rate, deceleration rate, steering angle, and inter-vehicle spacing.

This application discloses a computing system to implement vehicle localization in an assisted or automated driving system. The computing system can receive an environmental model populated with measurement data captured by sensors mounted in a vehicle. The computing system can detect a location of the vehicle relative to the map data based on a correlation between the measurement data and the map data. The computing system can detect landmarks in the map data and switch to sparsely-populated map data from higher-definition map data for subsequent location detections. When the computing system does not detect a vehicle location, the computing system can track movement of the vehicle based on subsequent measurement data in the environmental model. After reacquiring a vehicle location, the computing can realign the tracked movement of the vehicle and measured data to the map data or modify the map data to include the tracked data.

A parking assistance system for a vehicle includes: an object detection apparatus; at least one processor; and a computer-readable medium having stored thereon instructions that, when executed by the at least one processor, cause the at least one processor to perform operations that include: generating, through the object detection apparatus, information regarding an object located outside the vehicle; determining, based on the information regarding the object located outside the vehicle, that a parking space available outside the vehicle is a diagonal parking space; and generating a diagonal parking path for the vehicle to enter the parking space available outside the vehicle.

Various embodiments relate generally to autonomous vehicles and associated mechanical, electrical and electronic hardware, computer software and systems, and wired and wireless network communications to provide an autonomous vehicle fleet as a service. More specifically, systems, devices, and methods are configured to simulate navigation of autonomous vehicles in various simulated environments. In particular, a method may include receiving data representing characteristics of a dynamic object, calculating a classification of a dynamic object to identify a classified dynamic object, identifying data representing dynamic-related characteristics associated with the classified dynamic object, forming a data model of the classified dynamic object, simulating a predicted range of motion of the classified dynamic object in a simulated environment to form a simulated dynamic object, and simulating a predicted response of a data representation of a simulated autonomous vehicle.

Methods, apparatuses, systems, and non-transitory computer readable storage media for generating risk indicators are described. The disclosed technology includes determining a vehicle route of a vehicle and external object routes of external objects. The vehicle route is determined using vehicle route data including a vehicle location and a vehicle destination. The external object routes are determined using external object route data including external object locations and external object destinations. Based on a comparison of the vehicle route data and the external object route data, external object routes that satisfy a proximity criterion are determined. Risk data for the vehicle is generated based on a vehicle state of the vehicle and external object states of the external objects corresponding to the external object routes that satisfy the proximity criterion. In response to determining that the risk data satisfies a risk criterion, at least one risk indicator is generated.

Methods and systems for repairing a malfunctioning autonomous vehicle (AV) or semi-autonomous vehicle (SAV) are described herein. The AV or SAV may determine that an autonomous feature or sensor is malfunctioning and the extent of the damage to the autonomous feature or sensor. Then the AV or SAV may compare the extent of the damage to a predetermined threshold to determine whether the AV or SAV remains serviceable or otherwise road worthy. If the AV or SAV remains serviceable, the AV or SAV may locate the nearest repair facility having the necessary electronic components in stock and technical expertise for repairing the AV or SAV. Then the AV or SAV may request the nearest repair facility to send an autonomous repair vehicle to the current location of the AV or SAV to facilitate repair.

A vehicle driving assistance apparatus that includes: a sensing unit configured to sense an object outside the vehicle; and a processor configured to: obtain surrounding situation information, based on a location of the object outside the vehicle, determine whether the object approaches the vehicle from a traveling lane or a lateral lane, based on the determination of whether the object approaches the vehicle from a traveling lane or a lateral lane, generate a control signal that is configured to control at least one of a drive apparatus of the vehicle, a steering apparatus of the vehicle, and/or a brake apparatus of the vehicle (i) to avoid collision between the vehicle and the object or (ii) to perform an action that reduces an impulse on the vehicle from the collision, and provide the control signal to a vehicle control system of the vehicle.

A vehicle control device including a communication unit configured to obtain a current position of a vehicle; a sensing unit configured to sense another vehicle subject to a driving regulation and obtain current position of the other vehicle; a processor; and a computer-readable medium coupled to the processor having stored thereon instructions which, when executed by the processor, causes the processor to perform operations including autonomously driving the vehicle based on the respective current positions of the vehicle and the other vehicle in compliance with the driving regulation.