Systems and methods for parallel autonomy of a vehicle are disclosed herein. One embodiment receives input data, the input data including at least one of sensor data and structured input data; encodes the input data into an intermediate embedding space using a first neural network; inputs the intermediate embedding space to a first behavior model and a second behavior model, the first behavior model producing a first behavior output, the second behavior model producing a second behavior output; combines the first behavior output and the second behavior output using an ideal-behavior model to produce an ideal behavior for the vehicle; and controls one or more aspects of operation of the vehicle based, at least in part, on the ideal behavior.

Methods and systems for autonomous and semi-autonomous vehicle routing are disclosed. Roadway suitability for autonomous operation is scored to facilitate use in route determination. Maps of roadways suitable for various levels of autonomous operation may be generated. Such map data may be used by autonomous vehicles or other computer devices in determining routes based upon criteria for vehicle trips. Such routes may be automatically updated based upon changes in road conditions, vehicle conditions, operator conditions, or environmental conditions. Emergency routing using such map data is described, such as automatic routing and travel when a passenger is experiencing a medical emergency.

Methods and systems for autonomous and semi-autonomous vehicle routing are disclosed. Roadway suitability for autonomous operation is scored to facilitate use in route determination. Maps of roadways suitable for various levels of autonomous operation may be generated. Such map data may be used by autonomous vehicles or other computer devices in determining routes based upon criteria for vehicle trips. Such routes may be automatically updated based upon changes in road conditions, vehicle conditions, operator conditions, or environmental conditions. Emergency routing using such map data is described, such as automatic routing and travel when a passenger is experiencing a medical emergency.

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.

In a vehicle dispatch management system which manages a plurality of shared vehicles (2) and dispatches the shared vehicles in response to a dispatch request from a user, the vehicle dispatch management system includes: an individual cruising distance calculation device (22) configured to calculate a cruising distance of each of the shared vehicles; a total cruising distance calculation device (17) configured to calculate a total cruising distance of all of the shared vehicles; an energy supply determination device (18) configured to determine a necessity of traveling energy supply of the shared vehicle when the total cruising distance is equal to or less than a first predetermined value; and a supply instruction device (19) configured to instruct a supply instruction to the shared vehicle determined to be supplied on the basis of determining the necessity of traveling energy supply of the shared vehicle.

An autonomous driving system controls autonomous driving of a vehicle and calculates an interest level of a driver of the vehicle for the autonomous driving. In a driver-oriented mode, the autonomous driving system gives priority to at least one of setting by the driver, will of the driver, and ride quality. In a performance-oriented mode, the autonomous driving system gives priority to at least one of increase in a safety margin, reduction in a time to arrive at a destination, improvement in fuel economy, improvement in motion performance, and reduction in a frequency of notification to the driver. The autonomous driving system controls the autonomous driving in the driver-oriented mode in a case of a normal state, and controls the autonomous driving in the performance-oriented mode in a case of a low interest state where the interest level is lower than the normal state.

A system for providing a speed profile of a self-driving vehicle includes a vehicle driving information prediction device, and a speed profile generation device, wherein the vehicle driving prediction device includes a navigation unit configured to set information on a drive route and a target travel time, a 3D map information provision unit configured to search for gradient information of the drive route set by the navigation unit, and a vehicle driving information provision unit, and wherein the speed profile generation device includes a vehicle energy consumption calculation unit configured to calculate energy consumption at a current speed of the vehicle when the vehicle runs along the set drive route, and a speed profile calculation unit configured to calculate a distance-based target speed profile by executing a dynamic programming algorithm.

A vehicle travel control apparatus configured to control an actuator for driving a vehicle with a self-driving capability so that the vehicle follows a forward vehicle in front of the vehicle. The vehicle travel control apparatus includes a travel state detector configured to detect a traveling state of the forward vehicle, and an electric control unit having a microprocessor and a memory. The microprocessor is configured to perform determining whether the forward vehicle is cruising based on the traveling condition detected by the travel state detector, and controlling the actuator so that the vehicle travels in a normal mode, when it is determined that the forward vehicle is not cruising, and the vehicle follows the forward vehicle in a cruise mode with a fuel economy performance or quietness higher than in the normal mode, when the forward vehicle is cruising.

A method and a processor for controlling in-lane movement of a Self-Driving Vehicle (SDV) are provided. The method comprises: acquiring initial kinematic data associated with an obstacle; determining future kinematic data associated with the obstacle; acquiring initial kinematic data associated with the SDV; determining future kinematic data associated with the SDV which is indicative of at least two candidate future states of the SDV at a future moment in time; determining at least two candidate state-transition datasets for the SDV to transition from the initial state of the SDV to a respective one of the at least two candidate future states of the SDV using only in-lane movement; determining, by the electronic device, an energy efficiency score for a respective one of the at least two candidate state-transition datasets; determining a target state-transition dataset for the SDV based on respective energy efficiency scores.

A method for controlling a vehicle includes: receiving, by a controller, route data, wherein the route data is continuously updated while the vehicle is moving, and the vehicle includes a plurality of vehicle operating modes; receiving, by the controller, feature data, wherein the feature data is information about a plurality of features needed for each of the plurality of vehicle operating modes; determining, by the controller, a plurality of ranges for each of the plurality of vehicle operating modes, wherein each of the plurality of ranges is a function of the route data and the feature data for each of the plurality of vehicle operating modes; and commanding, by the controller, a user interface to display a list of range-mode combinations, wherein the list of range-mode combinations includes the plurality of ranges for each of the plurality of vehicle operating modes.