The subject disclosure relates to techniques for enabling autonomous vehicles to reason about similarities of features between drivable areas. A process of the disclosed technology can include receiving first sensor data corresponding with a first roadway segment, receiving second sensor data corresponding with a second roadway segment, encoding the first sensor data into a first vector, wherein the first vector represents a first roadway characteristic associated with the first roadway segment, encoding the second sensor data into a second vector, wherein the second vector represents a second roadway characteristic associated with the second roadway segment, and determining a similarity between the first roadway segment and the second roadway segment based on the first vector and the second vector.

A vehicle configured to operate in an autonomous mode may operate a sensor to determine an environment of the vehicle. The sensor may be configured to obtain sensor data of a sensed portion of the environment. The sensed portion may be defined by a sensor parameter. Based on the environment of the vehicle, the vehicle may select at least one parameter value for the at least one sensor parameter such that the sensed portion of the environment corresponds to a region of interest. The vehicle may operate the sensor, using the selected at least one parameter value for the at least one sensor parameter, to obtain sensor data of the region of interest, and control the vehicle in the autonomous mode based on the sensor data of the region of interest.

A driving assist system assists driving of a vehicle. A deceleration target includes at least one of a preceding vehicle, a mandatory stop line, a mandatory stop sign, a traffic signal, and a stop line before the traffic signal that exist ahead of the vehicle. A risk factor includes at least one of a pedestrian, a bicycle, a motorcycle, an oncoming vehicle, and a parked vehicle that exist ahead of the vehicle. The driving assist system executes: deceleration assist control that automatically decelerates the vehicle before the deceleration target; and risk avoidance control that automatically performs at least one of steering and deceleration of the vehicle so as to avoid the risk factor. When both the deceleration assist control and the risk avoidance control operate concurrently, the driving assist system notifies a driver of the vehicle of not the deceleration target but the risk factor.

A system includes a first notifying unit, a detection unit, and a second notifying unit. The first notifying unit performs a first notification for notifying a target in a predetermined area of an abnormality, in a case where an abnormal state occurs in a running vehicle. The detection unit detects presence of a target unaware of the first notification. The second notifying unit performs a second notification for notifying the target in the predetermined area of the abnormality, in a case where the presence of the target unaware of the first notification is detected.

A system includes a vehicle control module, a vehicle gateway module, and a wired vehicle communications network communicatively coupling the vehicle gateway module to the vehicle control module. The vehicle control module is programmed to receive sensor data from at least one sensor, execute a machine-learning program trained to determine whether the sensor data satisfies at least one criterion, and transmit the sensor data satisfying the at least one criterion to the vehicle gateway module. The vehicle gateway module is programmed to transmit the machine-learning program to the vehicle control module; upon receiving the sensor data from the vehicle control module, store the sensor data; and upon establishing a connection with a remote server, transmit the sensor data to the remote server.

An autonomous vehicle control system includes one or more processors. The one or more processors are configured to cause a transmitter to transmit a transmit signal from a laser source. The one or more processors are configured to cause a receiver to receive a return signal reflected by an object. The one or more processors are configured to cause one or more optics to generate a first polarized signal of the return signal with a first polarization, and generate a second polarized signal of the return signal with a second polarization. The one or more processors are configured to calculate a value of reflectivity based on a signal-to-noise ratio (SNR) value of the first polarized signal and an SNR value of the second polarized signal. The one or more processors are configured to operate a vehicle based on the value of reflectivity.

A method for child forward collision warning, the method may include sensing sensed information about an environment of a vehicle; detecting, based on the sensed information, a situation related to the environment; detecting, based on the sensed information, one or more children within the environment; classifying each child of the one or more children to a class; predicting, using a machine leaning process, a future behavior of the one or more children and an impact of the future behavior of the one or more children on a future progress of the vehicle; wherein the predicting of each child of the one or more children is responsive to a class of the child and to the situation; and responding to the predicting.

Systems and methods for hailing a vehicle for a ride or shipping a parcel. A user hails a vehicle with a gesture or app. A hailed vehicle carries the user to a destination or picks up a parcel from the user and performs shipping procedures.

According to some embodiments, a system receives a captured image perceiving an environment of an ADV from an image capturing device of the ADV. The system identifies an obstacle in motion near the ADV based on the captured image. The system predicts a location for the moving obstacle at each of a number of time points. The system generates a probability ellipse based on the predicted location at the each time point, where the probability ellipse includes a probability indicator indicating different probabilities of the moving obstacle for different locations within the probability ellipse at the each time point.

A method for planning traveling paths for multiple automatic harvesters, includes performing detection and forming basic agricultural land information by a detection device and receiving the basic agricultural land information at a path planning module. The path planning module sets quantity setting information and divides harvesting areas of the multiple automatic harvesters and travel paths thereof.