Techniques and methods for determining regions. For instance, a vehicle may determine a trajectory of the vehicle and a trajectory of an agent, such as a pedestrian. The vehicle may then determine one or more contextual factors. In some examples, the one or more contextual factors are associated with a location of the agent with respect to a crosswalk, a location of the vehicle with respect to the crosswalk, a state of the crosswalk, and/or the like. The vehicle may then determine the region using the trajectory of the vehicle, the trajectory of the agent, and the one or more contextual factors. Additionally, using a time buffer value and a distance buffer value associated with the region, the vehicle may determine whether to yield to the agent within the region.

A method according to some embodiments includes sensing, by a sensor set of the ego vehicle, a remote vehicle to generate sensor data describing driving behavior of the remote vehicle. The method further includes classifying a type of the remote vehicle based on the sensor data. The method further includes retrieving a behavior profile for the type of the remote vehicle that identifies criteria for the type of the remote vehicle and classifications of abnormal behavior and normal behavior based on the criteria. The method further includes comparing the behavior of the remote vehicle to the behavior profile for the type of the remote vehicle. The method further includes determining that the behavior of the remote vehicle is normal based on the comparing.

A method for operating a driving assistance system. The method includes operating at least one sensor device in a host vehicle for monitoring the surroundings of the host vehicle; recognizing a fast-moving vehicle that is traveling in a first lane adjacent to a second lane in which the host vehicle is traveling, and determining a travel speed of the fast-moving vehicle in a travel direction of the host vehicle, based on a travel speed of the host vehicle or of a following vehicle or of a preceding vehicle, by the sensor device and by a control device; identifying a movement of the fast-moving vehicle as a passing operation of the host vehicle or of the following vehicle or of the preceding vehicle; and identifying a change in the movement of the fast-moving vehicle as an abortion of the passing operation.

A vehicle control apparatus includes circuitry for controlling a vehicle. The circuitry is configured to search for a stop location where the vehicle is to stop, on a basis of road shoulder region information, generate an evacuation path on a basis of road information to the stop location, and guide the vehicle to the stop location from a first travel lane adjacent to a road shoulder region. The circuitry is further configured to calculate a collision risk of collision with an on-road obstacle. When the collision risk is a predetermined degree or higher, the circuitry is to interrupt the guidance, otherwise the circuitry is to guide the vehicle to enter the stop location and stop the vehicle at the stop location.

A method of simulating a sensor in an autonomous driving simulation includes obtaining values for a plurality of attributes of a target object to be sensed by a sensor simulator in the autonomous driving simulation. The sensor simulator may simulate an active sensor that outputs rays to an object and receives reflections of the rays from the object. The method also includes inputting the obtained values for the plurality of attributes to a predetermined reflection rate table to obtain a reflection rate mapped to the obtained values; and generating sensor data corresponding to the target object based on the obtained reflection rate.

A method for estimating a future fall of a 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 actual or estimated cargo falling events and generate one or more future falling cargo predictors for multiple types of cargo; estimating, from the sensed information, an impact of cargo falling events related to at least some of the types of cargo; and responding to the estimating, wherein the responding comprises at least one out of (a) storing the one or more future falling cargo predictors for the multiple types of cargo, (b) transmitting the one or more future falling cargo predictors for the multiple types of cargo; (c) storing the estimated impact of cargo falling events related to the at least some of the types of cargo, and (d) transmitting the impact of cargo falling events related to the at least some of the types of cargo.

In an autonomous driving apparatus and method, the apparatus includes a sensor unit, an output unit, a memory, and a processor configured to control the autonomous driving of an ego vehicle based on a map information stored in the memory. The processor generates an actual driving trajectory and an expected driving trajectory of a surrounding vehicle around the ego vehicle based on driving information of the surrounding vehicle detected by the sensor unit and the map information and controls one or more of the driving of the ego vehicle and provides communication with an external organization, based on a state of a passenger detected by the sensor unit when an autonomous driving mode of the ego vehicle is turned off, and based on an autonomous driving risk of the ego vehicle determined based on a trajectory error between the actual driving trajectory and expected driving trajectory of the surrounding vehicle.

Systems and methods for general driving behavior of an autonomous vehicle are disclosed. In one aspect, an autonomous vehicle includes a trailer, at least one perception sensor, a non-transitory computer readable medium, and a processor. The processor is configured to estimate a grade of the roadway based on the perception data, provide a first control input to the autonomous vehicle based on the grade of the roadway, determine a response of the autonomous vehicle to the first control input based on the perception data, estimate a trailer load of the trailer based on the response of the autonomous vehicle to the first control input, and provide a second control input to the autonomous vehicle based on the grade of the roadway and the trailer load.

A high precision digital map is pre-developed and stored in a memory of an in-vehicle control computer on an autonomous vehicle. The digital map is updated by the in-vehicle control computer with detected roadway data that is a fusion of roadway perception data from at least one perception sensor on the autonomous vehicle and real time GPS signal from at least one GPS receiving devices on the autonomous vehicle. The updated digital map is transferred to a remote oversight system via a network communication subsystem, and the oversight system distributes the updated digital map to other autonomous vehicles connected over the network communication subsystem.

Systems and methods for detecting physical infrastructure related to navigation of an autonomous vehicle are disclosed. In one aspect, the autonomous vehicle includes a perception sensor configured to generate perception data, a non-transitory computer readable medium, and a processor. The processor is configured to determine a minimal risk condition (MRC) maneuver for the autonomous vehicle to execute, identify a safe zone in which the autonomous vehicle is able to execute the MRC maneuver by coming to a stop based on the perception data, identify one or more exclusion zones within the safe zone based on the perception data, and control the autonomous vehicle to execute the MRC maneuver including stopping outside of the exclusion zone.