Embodiments described herein may provide a method for using vehicle sensor data to identify where road work exists within a road network. Methods may include: receiving probe data and sensor data from a plurality of probe apparatuses traveling along a sequence of road segments; identifying, from the sensor data, one or more indicators of a beginning of a road work area; identifying, from the sensor data, binary indicators of the presence of road work or a lack of presence of road work along the sequence of road segments; and determining, based on the one or more indicators of a beginning of a road work area and the binary indicators of the presence of road work or the lack of road work along the sequence of road segments, a probability of road work occurring along one or more road segments of the sequence of road segments.

A surrounding environment information generating unit acquires surrounding position information indicating a measured position of a side wall including a side wall of an evacuation spot, and generates measured map information for the side wall using the surrounding position information. When driving operation by a driver cannot be performed, a corrected map information generating unit calculates a correction value obtained by multiplying a difference between a distance from a traveling center line of a host vehicle to the side wall in base map information and that in the measured map information by a reliability, and generates corrected map information obtained by applying the correction value to the base map information. A route generating unit generates an evacuation travel route with the evacuation spot as a destination using the corrected map information. A vehicle control unit outputs a vehicle speed command for the host vehicle using the evacuation travel route.

An approach is provided for determining bicycle lane deviations for autonomous vehicle warning or operation. The approach, for example, involves retrieving probe data associated with a bicycle transportation mode. The approach also involves determining a plurality of probe points of the probe data that are map-matched outside of a bicycle lane. The approach further involves clustering the plurality of probe points into at least one location cluster. The approach further involves storing the one or more location clusters in a geographic database as respective one or more hazard areas where a plurality of bicycles deviates outside of the bicycle lane. By way of example, the approach can further involve using the at least one location cluster to perform at least one of providing a warning message or determining a driving parameter for an autonomous vehicle.

Disclosed herein are systems and method including a method for managing an autonomous vehicle. The method include providing as first input to a machine learning model a raster image and a vector associated with a context of a scene comprising an autonomous vehicle and a plurality of agents, providing as second input to the machine learning model a planned travel path for the autonomous vehicle, based the first input and the second input, outputting from the machine learning model a plurality of yield/assert predictions, wherein the plurality of yield/assert predictions comprises a respective yield/assert prediction related to whether to yield or to assert in relation to each respective agent of the plurality of agents and causing the autonomous vehicle to travel along the planned travel path while yielding or asserting against the plurality of agents according to the plurality of yield/assert predictions.

Systems and methods are disclosed for navigating a host vehicle. In one implementation, at least one processor may be programmed to receive images representative of an environment of the host vehicle; identify an oncoming target vehicle associated with a projected trajectory indicated by an aspect of the oncoming target vehicle in the images; determine that a planned trajectory for the host vehicle crosses the projected trajectory of the oncoming target vehicle and indicates a potential turn-across-path event; determine a remedial action for the host vehicle in response to the oncoming target vehicle and the potential turn-across-path event; implement the remedial action if the oncoming target vehicle is determined to be not approaching a road feature that negates the potential turn-across-path event; and forego the remedial action if the oncoming target vehicle is determined to be approaching a road feature that negates the potential turn-across-path event.

Systems and methods for an autonomous vehicle are provided. In one aspect, an autonomous vehicle includes a perception sensor and a processor configured to: receive detected roadway conditions data including roadway grade data from the perception sensor, retrieve mapped data having grade data, and determine that the roadway has a grade based on the detected roadway grade data and the retrieved roadway grade data. The processor can be further configured to, in response to determining that the roadway has a grade, determine that the grade of the roadway is greater than or equal to a predetermined high grade value and less than a predetermined grade limit, and in response to determining that the grade of the roadway is greater than or equal to the predetermined high grade value and less than the predetermined grade limit, operate the autonomous vehicle to change lane to a right-most lane.

ADAS designed to assist a driver of a motor-vehicle in low meteorological visibility conditions, in particular with fog, comprising a sensory system comprising a front vision system arranged on the motor-vehicle to monitor an environment in front of the motor-vehicle and comprising one or different first front cameras designed to operate in the electromagnetic spectrum visible to the human eye, and one or different second front cameras designed to operate in the electromagnetic spectrum invisible to the human eye; and electronic processing resources communicatively coupled to the sensory system to receive and process outputs of one or more of the automotive front cameras to determine a meteorological visibility in front of the motor-vehicle and assist the driver of the motor-vehicle based on the meteorological visibility in front of the motor-vehicle. Assisting the driver of the motor-vehicle comprises differentially controlling operation(s) of one or different automotive systems comprising an external lighting system, a user interface, and a cruise control system based on the meteorological visibility in front of the motor-vehicle. Assisting the driver of the motor-vehicle further comprises visually assisting the driver of the motor-vehicle via the user interface by displaying on at least one automotive display thereof either a video streaming of an automotive front camera or a virtual depiction of an environment in front of or surrounding the motor-vehicle computed based on information from the sensory system.

In accordance with an exemplary embodiment, methods and systems are provided for controlling steering of an autonomous vehicle. The method includes: operating, by a processor, the autonomous vehicle in a path-based automated driving assist mode; receiving, by the processor, driver input including a driver torque; classifying, by the processor, an operation mode based on a type of the path-based automated driving assist mode; determining, by the processor, an override threshold for overriding the path-based automated driving assist mode on a first lateral side of the autonomous vehicle based on the operation mode; determining, by the processor, a driver override status based on the override torque threshold; and generating, by the processor, control signals to control the steering of the autonomous vehicle based on the driver override status and the driver torque.

A method for autonomous operation of a vehicle on a driving route ahead permits autonomous operation of the vehicle is only permitted if one or a group of conditions is/are fulfilled for a predetermined route length of the driving route ahead. The conditions include: there is a structural separation on at least one side of a current travel path of the vehicle; a driving lane of the vehicle has a minimum lane width; there are no humps and dips substantially limiting the range of environmental detection sensors; the number of driving lanes does not change; there are no tunnels; there are no buildings on the travel path; there are no motorway junctions; a radius of curvature of the driving lane of the vehicle is larger than a predetermined limit value; there are no traffic disruptions; there are no traffic reports about dangerous situations; and there are no traffic reports about the presence of roadworks.

When a vehicle performing driverless operation of encounters a blockade situation, a probable blockade time duration of the blockade situation is predicted based on a situational analysis. Support by a teleoperator is requested when the predicted blockade time duration of the blockade situation is greater than a predetermined time duration or when the vehicle has waited longer than the predicted blockade time duration for a resolution of the blockade situation.