A vehicle safety support apparatus includes: a driver monitoring sensor configured to monitor a driver; an external environment monitoring sensor configured to monitor an external environment of a vehicle; a driver input filtering unit configured to filter a vehicle control input from the driver; and at least one processor connected to the driver monitoring sensor, the external environment monitoring sensor, and the driver input filtering unit, the at least one processor configured to: determine criterion based on data acquired from the driver monitoring sensor and the external environment monitoring sensor; determine whether to take over a driving control of the vehicle in response to the data acquired from the driver monitoring sensor and the external environment monitoring sensor meeting the criterion; and perform autonomous driving to move the vehicle to a safe area in response to determining to take over the driving control from the driver.

An approach is provided for operating a vehicle using vulnerable road user data. The approach, for example, involves determining a road link on which the vehicle is traveling or expects to travel. The approach also involves querying a geographic database for a vulnerable road user attribute of the road link. The approach further involves providing a notification to activate or deactivate an autonomous driving mode of the vehicle, a vehicle sensor of the vehicle, or a combination thereof while the vehicle travels on the road link based on determining that the vulnerable road user attribute meets a threshold criterion.

An on-board sensor system includes: a first sensor configured to detect a situation around a vehicle; a second sensor having a higher angular resolution than the first sensor; an acquisition unit configured to acquire a detection result of the first sensor; and a range decision unit configured to decide, based on the detection result, an observation range to be observed by the second sensor around the vehicle.

A system includes a wireless monitor station. The wireless station monitors a region in which one or more vehicles frequent such as automobiles, trains, etc. The wireless station receives a wireless communication indicating presence of a first vehicle in the monitored region. The first communication includes a unique identifier value assigned to the first vehicle. Based on detected presence of the first vehicle in the monitored region, the wireless station controls traffic flow associated with the monitored region.

A system for reconstructing a vehicular crash (i) receives sensor data of a vehicular crash from at least one mobile device associated with a user; (ii) generates a scenario model of the vehicular crash based upon the received sensor data; (iii) transmits the scenario model to a user computer device associated with the user; (iv) receives a confirmation of the scenario model from the user computer device; (v) stores the scenario model; and (vi) may generate at least one insurance claim form based upon the scenario model. As a result, the speed and accuracy of the claim processing 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 method, system and device are disclosed for determining safety conflicts in redundant subsystems of autonomous vehicles. Each redundant subsystem calculates a world model or path plan, including locations, dimensions, and orientations of moving and stationary objects, as well as projected travel paths for moving objects in the future. The travel paths and projected future world models are subsequently compared using a geometric overlay operation. If at future time moments the projected world models match within predefined margins, the comparison results in a match. In case of a mismatch at a given future moment between projected world models, a determination is made as to whether the autonomous vehicle and all road users in this future moment are safe from collision or driving off the drivable space or road based on a geometric overlay operation.

In order to avoid a collision between a vehicle (1) and other traffic, an on-board system (100) of the vehicle (1) scans for target entities (2) in at least one lane (5, 6) to a side of the vehicle (1) and determines position and state of motion of detected target entities (2). From a state of the vehicle (1) an intention of a driver of the vehicle (1) to move the vehicle (1) into one of the at least one lanes (5, 6) scanned is inferred. If the on-board system (100) detects a risk of collision between a target entity (2) and the vehicle (1), then, if such an intention of the driver is found, the motion of the vehicle (1) is impeded by the system (100), applying brakes (200) of the vehicle (1) and/or reducing a driving torque of the vehicle (1). A speed of the vehicle (1) is monitored and a motion of the vehicle (1) is not impeded if the speed of the vehicle (1) is above a pre-defined threshold.

The rise of the connected objects known as the “Internet of Things” (IoT) will rival past technological marvels. This application discloses an object control system (OCS) to control movement of an object in a smart environment. The object control system uses a virtualized shared database and a shared object management center to control the navigation and protection of moving and flying objects through an IoT network utilizing IoT devices. It also uses time of day to schedule activities of the moving, flying, stationary and fixed objects in the smart environment to allow all objects within object control system operate freely with no interference and collision.

Systems and methods are provided for crowdsourcing a sparse map for autonomous vehicle navigation. In one implementation, a non-transitory computer-readable medium may include a sparse map for autonomous vehicle navigation along a road segment. The sparse map may include at least one line representation of a road surface feature extending along the road segment, each line representation representing a path along the road segment substantially corresponding with the road surface feature, and wherein the road surface feature is identified through image analysis of a plurality of images acquired as one or more vehicles traverse the road segment and a plurality of landmarks associated with the road segment.

In various examples, systems and methods are disclosed that preserve rich, detail-centric information from a real-world image by augmenting the real-world image with simulated objects to train a machine learning model to detect objects in an input image. The machine learning model may be trained, in deployment, to detect objects and determine bounding shapes to encapsulate detected objects. The machine learning model may further be trained to determine the type of road object encountered, calculate hazard ratings, and calculate confidence percentages. In deployment, detection of a road object, determination of a corresponding bounding shape, identification of road object type, and/or calculation of a hazard rating by the machine learning model may be used as an aid for determining next steps regarding the surrounding environment—e.g., navigating around the road debris, driving over the road debris, or coming to a complete stop—in a variety of autonomous machine applications.