This route prediction device includes: an information acquisition circuitry to acquire a position and a speed of an own vehicle, positions and speeds of surrounding vehicles traveling around the own vehicle, and map information around the own vehicle; a cut-in determinator to determine whether or not the surrounding vehicle will cut in onto a traveling lane of the own vehicle, on the basis of an inducing factor of inducing cut-in of another vehicle; an assumptive vehicle setting circuitry to determine a traveling position, on a road, of an assumptive vehicle assumed to influence traveling of a cut-in vehicle determined to cut in, among the surrounding vehicles, using road information obtained from the map information; and a route prediction circuitry to predict a traveling route of one of the surrounding vehicles, on the basis of the traveling position of the assumptive vehicle, and so forth.

The technology relates to determining general weather conditions affecting the roadway around a vehicle, and how such conditions may impact driving and route planning for the vehicle when operating in an autonomous mode. For instance, the on-board sensor system may detect whether the road is generally icy as opposed to a small ice patch on a specific portion of the road surface. The system may also evaluate specific driving actions taken by the vehicle and/or other nearby vehicles. Based on such information, the vehicle's control system is able to use the resultant information to select an appropriate braking level or braking strategy. As a result, the system can detect and respond to different levels of adverse weather conditions. The on-board computer system may share road condition information with nearby vehicles and with remote assistance, so that it may be employed with broader fleet planning operations.

Methods and systems for monitoring use and determining risks associated with operation of a vehicle having one or more autonomous operation features are provided. According to certain aspects, operating data may be recorded during operation of the vehicle. This may include information regarding the vehicle, the vehicle environment, use of the autonomous operation features, and/or control decisions made by the features. The control decisions may include actions the feature would have taken to control the vehicle, but which were not taken because a vehicle operator was controlling the relevant aspect of vehicle operation at the time. The operating data may be recorded in a log, which may then be used to determine risk levels associated with vehicle operation based upon risk levels associated with the autonomous operation features. The risk levels may further be used to adjust an insurance policy associated with the vehicle.

Among other things, we describe techniques for implementing a vehicle response to sensor failure. In general, one innovative aspect of the subject matter described in this specification can be embodied in methods that include receiving information from a plurality of sensors coupled to a vehicle, determining that a level of confidence of the received information from at least one sensor of a first subset of sensors of the plurality of sensors is less than a first threshold, comparing a number of sensors in the first subset of sensors to a second threshold, and adjusting the driving capability of the vehicle to rely on information received from a second subset of sensors of the plurality of sensors, wherein the second subset of sensors excludes the at least one sensor of the first subset of sensors.

An automated speed control system includes a ranging-sensor, a camera, and a controller. The ranging-sensor detects a lead-speed of a lead-vehicle traveling ahead of a host-vehicle. The camera detects an object in a field-of-view. The controller is in communication with the ranging-sensor and the camera. The controller is operable to control the host-vehicle. The controller determines a change in the lead-speed based on the ranging-sensor. The controller reduces a host-speed of the host-vehicle when the lead-speed is decreasing, no object is detected by the camera, and while a portion of the field-of-view is obscured by the lead-vehicle.

Autonomous vehicles may utilize neural networks for image classification in order to navigate infrastructures and foreign environments, using context dependent transfer learning adaptation. Techniques include receiving a transferable output layer from the infrastructure, which is a model suitable for the infrastructure and the local environment. Sensor data from the autonomous vehicle may then be passed through the neural network and classified. The classified data can map to an output of the transferable output layer, allowing the autonomous vehicle to obtain particular outputs for particular context dependent inputs, without requiring further parameters within the neural network.

Systems and methods relating to controlling a driving system operatively coupled to a vehicle are disclosed. A location is identified using one or more sensors included with the vehicle. An input of the driving system is identified using the location. A desired output of the driving system is determined using the input.

A moving object escape prevention method includes: controlling, by a processor of a moving object, to drive the moving object based on autonomous driving; detecting, by the processor, whether a collision occurred by the moving object; in response to detecting the collision, transmitting, by the processor, a collision occurrence notification signal and position information of the moving object to an Intelligent Transportation System Infrastructure (ITSI); receiving, by the processor, escape-related information from the ITSI. The receiving escape-related information includes: determining, by the ITSI, whether or not the moving object escapes based on position information of the moving object; receiving, by the processor, accident handling information from the ITSI upon determining that the moving object does not escape, and receiving, by the processor, an escape warning message from the ITSI when the position information of the moving object changes.

A driving mode transitioning system and method for supporting transitioning to an unsupervised autonomous driving mode of an Automated Driving System, ADS, of a vehicle. The driving mode transitioning system obtains vehicle situational data indicating a state of vehicle surroundings along with position and heading of the vehicle; determines based on the obtained vehicle situational data, that unsupervised driving conditions of an unsupervised driving mode-related driving policy pertinent an unsupervised autonomous driving mode of the ADS, are complied with; determines that the ADS has active a supervised driving mode; implements the unsupervised driving mode-related driving policy to govern the supervised driving mode; and enables the unsupervised autonomous driving mode to be activated for the ADS, when positioning and/or velocity of the vehicle has reached compliance with unsupervised dynamic driving conditions of the unsupervised driving mode-related driving policy.

A vehicular trailer sway management system includes at least one rearward-sensing radar sensor disposed at a vehicle and an electronic control unit (ECU). Radar data captured by the at least one rearward-sensing radar sensor is provided to the ECU. With a trailer hitched to the vehicle, the trailer sway management system, via processing at the ECU of the provided captured radar data, determines oblique angles of the trailer relative to the vehicle. As the vehicle tows the trailer, and responsive to monitoring of determined oblique angles of the trailer relative to the longitudinal axis of the vehicle, the trailer sway management system determines sway of the trailer relative to the vehicle. Responsive to the determined sway of the trailer relative to the vehicle, the trailer sway management system at least in part controls operation of the vehicle to manage sway of the trailer relative to the vehicle.