A method for risk based processing, the method may include detecting, based on first sensed information sensed at a first period, a suspected risk within an environment of a vehicle; selecting, from reference information, a situation related subset of the reference information, wherein the situation related subset of the reference information is related to the situation; selecting, from reference information, a suspected risk related subset reference information, wherein the potential risk related subset of the reference information is related to the potential risk; and determining whether the suspected risk is an actual risk, based at least on part on the suspected risk related subset reference information.

A method for situation aware processing, the method may include detecting a situation, based on first sensed information at a first period; selecting, from reference information, a situation related subset of the reference information, wherein the situation related subset of the reference information is related to the situation; and performing, by a situation related processing unit, a situation related processing, wherein the situation related processing is based on the situation related subset of the reference information and on second sensed information sensed at a second period, wherein the situation related processing comprises at least one out of object detection and object behavior estimation,

A method for safe transfer between manned and autonomous driving modes, the method may include detecting, based on first sensed information sensed during a first period, a situation related to an environment of the autonomous vehicle; searching for one or more matching concepts of a group of reference concepts, to which the situation belongs, each reference concept of the group represents a plurality of situations and has a reference concept safety level; wherein for each reference concept of at least a sub-group of the group of reference concepts the safety level of the reference concept is based on a tested success level of at only some of the plurality of scenarios represented by the reference concept; and determining, based on an outcome of the searching, whether the vehicle is capable to safely autonomously drive through the environment.

V2V communication-less truck platooning.

A method for detecting fallen cargo, the method may include receiving by a computerized system, sensed information related to driving sessions of multiple vehicles; applying a machine learning process on the sensed information to detect fallen cargo and to classify the fallen cargo to fallen cargo classes; estimating, from the sensed information, an impact of at least some of the fallen cargo classes on a behavior of at least some of the multiple vehicles; and determining, based on the impact, at least one suggested vehicle behavior as a response to a detection of at least some of the fallen cargo classes.

A system for notifying emergency services of a vehicular crash may (i) receive sensor data of a vehicular crash from at least one mobile device associated with a user; (ii) generate a scenario model of the vehicular crash based upon the received sensor data; (iii) store the scenario model; and/or (iv) transmit a message to one or more emergency services based upon the scenario model. As a result, the speed and accuracy of deploying emergency services to the vehicular crash location is increased. The system may also utilize vehicle occupant positional data, and internal and external sensor data to detect potential imminent vehicle collisions, take corrective actions, automatically engage autonomous or semi-autonomous vehicle features, and/or generate virtual reconstructions of the vehicle collision.

A vehicle controlling apparatus includes a setting unit and an acquiring unit. The setting unit is configured to set a target inter-vehicle distance. The acquiring unit is configured to acquire position information of a temporary stopping location on a traveling route on which an own vehicle travels. The setting unit is configured to, on a condition that the own vehicle travels to follow a preceding vehicle, and that the acquiring unit has acquired the position information of the temporary stopping location, make a setting change to make the target inter-vehicle distance greater than a normal setting value when a distance from the own vehicle to the temporary stopping location reaches a predetermined distance, and make a setting change to bring the target inter-vehicle distance closer to the normal setting value in accordance with the distance until the own vehicle reaches the temporary stopping location.

The present disclosure provides systems and methods that employ tolerance values defining a level of vehicle control precision for motion control of an autonomous vehicle. More particularly, a vehicle controller can obtain a trajectory that describes a proposed motion path for the autonomous vehicle. A constraint set of one or more tolerance values (e.g., a longitudinal tolerance value and/or lateral tolerance value) defining a level of vehicle control precision can be determined or otherwise obtained. Motion of the autonomous vehicle can be controlled to follow the trajectory within the one or more tolerance values (e.g., longitudinal tolerance value(s) and/or a lateral tolerance value(s)) identified by the constraint set. By creating a motion control framework for autonomous vehicles that includes an adjustable constraint set of tolerance values, autonomous vehicles can more effectively implement different precision requirements for different driving situations.

The present disclosure provides systems and methods that employ tolerance values defining a level of vehicle control precision for motion control of an autonomous vehicle. More particularly, a vehicle controller can obtain a trajectory that describes a proposed motion path for the autonomous vehicle. A constraint set of one or more tolerance values (e.g., a longitudinal tolerance value and/or lateral tolerance value) defining a level of vehicle control precision can be determined or otherwise obtained. Motion of the autonomous vehicle can be controlled to follow the trajectory within the one or more tolerance values (e.g., longitudinal tolerance value(s) and/or a lateral tolerance value(s)) identified by the constraint set. By creating a motion control framework for autonomous vehicles that includes an adjustable constraint set of tolerance values, autonomous vehicles can more effectively implement different precision requirements for different driving situations.

A driving assistance apparatus includes a memory and a processor in communication with the memory and configured to execute instructions stored in the memory operable to determine at least one surrounding vehicle based on location information of the at least one surrounding vehicle, wherein the at least one surrounding vehicle is configured to receive at least one vehicle signal from a host vehicle. The processor is further configured to determine a line of sight (LOS) of the at least one surrounding vehicle based on the at least one vehicle signal, wherein the line of sight is defined by a perceivability of a driver of the at least one surrounding vehicle to view the at least one vehicle signal. The processor is further configured to determine a confidence level of the at least one surrounding vehicle based on the determined LOS.