A driving assistance system includes a sensor set, a data storage device and an output device. The sensor set detects a set of road users and, for each road user, a current state including a current speed and a current position. The data storage device includes a finite plurality of behavioral models. The data processor assigns a behavioral model to each road user, probabilistically estimates, for each road user, a belief state comprising a set of alternative subsequent states and corresponding probabilities, each alternative subsequent state including a speed and a position, according to the behavioral model assigned to each road user, and determines a risk of collision of the road vehicle with a road user, based on the probabilistically estimated future state of each road user. The output device outputs a driver warning signal or executes an avoidance action if the risk of collision exceeds a predetermined threshold.

A method controls a motor vehicle including a plurality of sensors for acquiring raw data relative to the environment of the vehicle and a computational unit for receiving the raw data acquired by the sensors. The method includes: the computational unit receives the raw data and processes the raw data to deduce therefrom pieces of information relative to the environment of the vehicle and coefficients of probability of error in the deduction of each piece of information, and settings for controlling the vehicle are generated depending on the pieces of information and the probability coefficients. For at least one of the sensors, a quality coefficient relative to the quality of the raw data sent by this sensor is determined, the reliability of the control settings is estimated, and a decision is made to correct or not correct the control settings depending on the estimated reliability of the control settings.

A cooperative adaptive cruise control (CACC) system acquires a driving pattern of a target vehicle and variably provides an inter-vehicle distance and a responsible speed level of a subject vehicle that are followed by the CACC system based on the driving pattern. The CACC system includes a communication unit receiving vehicle information and road information of a region in which the subject vehicle travels; an information collection unit collecting driving information of a forward vehicle, vehicle information of the subject vehicle, and the road information; and a control unit controlling the inter-vehicle distance and the responsible speed level of the CACC system based on the driving pattern of the target vehicle according to generated control information.

A processing system is for a motorized mobile system that provides powered mobility to one or more users. The motorized mobile system may consist, for example, of one or more of a mobile chair, a mobility scooter, an electronic conveyance vehicle, a riding lawn mower, a grocery cart, an all-terrain vehicle, an off-road vehicle, and a golf cart. The processing system comprises at least one sensor to measure one or more kinematic states of an object proximate to the motorized mobile system and at least one processor to use at least one object kinematic model as at least one state estimator. The at least one processor predicts a first kinematic state estimate of the object at a first time based on a prior knowledge of state for the object. The at least one processor uses the first kinematic state estimate of the object at the first time and a measured kinematic state observed by the sensor at a second time to determine a second kinematic state estimate of the object at the second time, wherein the first time is less than the second time. The at least one processor outputs the first kinematic state estimate at the first time from the object kinematic model for use by at least one other process of the motorized mobile system, wherein the at least one other process causes one or more actions to be taken by the motorized mobile system based on the first kinematic state estimate of the object at the first time. The at least one processor uses the second kinematic state estimate at the second time as an input to the object kinematic model to predict another kinematic state estimate of the object.

A cooperative adaptive cruise control (CACC) system acquires a driving pattern of a target vehicle and variably provides an inter-vehicle distance and a responsible speed level of a subject vehicle that are followed by the CACC system based on the driving pattern. The CACC system includes a communication unit receiving vehicle information and road information of a region in which the subject vehicle travels; an information collection unit collecting driving information of a forward vehicle, vehicle information of the subject vehicle, and the road information; and a control unit controlling the inter-vehicle distance and the responsible speed level of the CACC system based on the driving pattern of the target vehicle according to generated control information.

Systems, methods, computer-readable media, techniques, and methodologies are disclosed for performing end-to-end, learning-based keypoint detection and association. A scene graph of a signalized intersection is constructed from an input image of the intersection. The scene graph includes detected keypoints and linkages identified between the keypoints. The scene graph can be used along with a vehicle's localization information to identify which keypoint that represents a traffic signal is associated with the vehicle's current travel lane. An appropriate vehicle action may then be determined based on a transition state of the traffic signal keypoint and trajectory information for the vehicle. A control signal indicative of this vehicle action may then be output to cause an autonomous vehicle, for example, to implement the appropriate vehicle action.

Techniques are discussed for controlling a vehicle, such as an autonomous vehicle, to navigate a junction in an environment. In some cases, the techniques can be used to navigate a turn at the junction or traverse through the junction. Operations include the vehicle detecting a stop signal and preparing the vehicle to stop at a location associated with the junction. The vehicle can determine a time to execute a maneuver and a visibility distance that a sensor can observe in the environment. A speed of the vehicle to execute the maneuver can be determined based on the time to execute the maneuver and the visibility distance.

Systems and methods for controlling a failover response of an autonomous vehicle are provided. In one example embodiment, a method includes determining, by one or more computing devices on-board an autonomous vehicle, an operational mode of the autonomous vehicle. The autonomous vehicle is configured to operate in at least a first operational mode in which a human driver is present in the autonomous vehicle and a second operational mode in which the human driver is not present in the autonomous vehicle. The method includes detecting a triggering event associated with the autonomous vehicle. The method includes determining actions to be performed by the autonomous vehicle in response to the triggering event based at least in part on the operational mode. The method includes providing one or more control signals to one or more of the systems on-board the autonomous vehicle to perform the one or more actions in response to the triggering event.

A method for evaluating and/or influencing the driving behavior of a driver of a motor vehicle, involving a motor vehicle, in particular in local public transport or in inner-city traffic, preferably for use in vehicle fleets, is designed in such a manner that at least one parameter which describes the driving mode is determined, wherein the position of the gas pedal over time is determined as parameter, and wherein the state of the driving behavior, based on the determined position of the gas pedal, is visually displayed to the vehicle driver by means of a display system in the motor vehicle. Also disclosed is a corresponding device.

An apparatus includes a sensor, a navigation circuit and a control circuit. The sensor may be configured to generate surrounding road information of a road. The road may have a plurality of available lanes. The navigation circuit may be configured to determine a current position of the apparatus on the road. The control circuit may be configured to (i) access map data that identifies a number of the available lanes in the road proximate the position, (ii) determine a current lane among the available lanes that the apparatus is within based on all of the position, the surrounding road information and the map data and (iii) generate feedback data based on both the position and the current lane. The navigation device may be further configured to adjust the current position to a center of the current lane in response to the feedback data.