A method, including: acquiring a point cloud frame including point cloud data of a pedestrian; projecting the point cloud data of the pedestrian to a ground coordinate system to obtain projection point data of the pedestrian; determining a direction of a connection line between the two shoulders of the pedestrian, based on a location distribution of the projection point data; extracting a point cloud of a stable region from the point cloud data of the pedestrian based on the direction of the connection line between the two shoulders of the pedestrian, a form change range of the stable region when the pedestrian moves being smaller than form change ranges of other regions of the pedestrian; and determining movement information of the pedestrian based on a coordinate of a center point of the point cloud of the stable region in a plurality of consecutive point cloud frames.

Various embodiments of the invention enable an ADV to dynamically adjust its behaviors to emulate behaviors of a vehicle operated by a human driver when the ADV encounters an obstacle. A dynamic cost function can be used to collect real-time values of a set of parameters, and use the real-time values to constantly adjust a preferred safety distance where the ADV can be stopped ahead of the obstacle. An method includes determining a first distance to the obstacle in response to detecting an obstacle ahead of the ADV; and for each of a number of iterations, collecting a real-time value for each of a set of parameters, determining an offset to the first distance using the real-time value for each of the set of parameters, calculating a second distance based on the first distance and the offset, and controlling the ADV in view of the second distance using an expected value of each of the set of parameters, such that the ADV can stop at a point having the second distance to the obstacle.

A driving assistance apparatus includes a controller programmed to perform a deceleration assistance process of assisting in decelerating a vehicle before the vehicle arrives at a deceleration object, and to control a display apparatus to display, in a first display area, first notification information for notifying an occupant of the vehicle of the deceleration object that is a target for the deceleration assistance process. When a first object and a second object that is different from the first object are both detected as the deceleration object and the second object is the target for the deceleration assistance process but the first object is not the target for the deceleration assistance process, the controller is programmed to control the display apparatus to display, in a second display area, second notification information for notifying the occupant of the first object, the second display area is different from the first display area.

A travel controller recognizes an action of a driver of a vehicle from image data of the driver. The travel controller obtains information indicating that determination of whether autonomous driving of the vehicle is permissible cannot be given. When the information indicating that determination of whether the autonomous driving of the vehicle is permissible cannot be given is obtained, the travel controller operates a human interface to request the driver for an instruction to drive the vehicle. The travel controller determines whether the driver is giving an instruction to drive the vehicle from an action of the driver recognized in response to the request for an instruction to drive the vehicle. When determined in the determination process that the driver is giving an instruction to drive the vehicle, the travel controller operates a drive system of the vehicle to permit autonomous driving of the vehicle.

Aspects and implementations of the present disclosure address shortcomings of the existing technology by enabling motion pattern-assisted object classification of objects in an environment of an autonomous vehicle (AV) by obtaining, from a sensing system of the AV, a plurality of return points, each return point comprising one or more velocity values and one or more coordinates of a reflecting region that reflects a signal emitted by the sensing system, identifying an association of the plurality of return points with an object in an environment of the AV, identifying, in view of the one or more velocity values of at least some of the plurality of return points, a type of the object or a type of a motion of the object, and causing a driving path of the AV to be determined in view of the identified type of the object.

The technology employs a holistic approach to passenger pickups and other wayfinding situations. This includes identifying where passengers are relative to the vehicle and/or the pickup location. Information synthesis from different sensors, agent behavior prediction models, and real-time situational awareness are employed to identify the likelihood that the passenger to be picked up is at a given location at a particular point in time, with sufficient confidence. The system can provide adaptive navigation by helping passengers understand their distance and direction to the vehicle, for instance using various cues via an app on the person's device. Rider support tools may be provided, which enable a remote agent to interact with a customer via that person's device, such as using the camera on the device to provide wayfinding support to enable the person to find their vehicle. Ride support may also use sensor information from the vehicle when providing wayfinding support.

Aspects and implementations of the present disclosure address shortcomings of the existing technology by enabling Doppler-assisted segmentation of points in a point cloud for efficient object identification and tracking in autonomous vehicle (AV) applications, by: obtaining, by a sensing system of the AV, a plurality of return points comprising one or more velocity values and one or more coordinates of a reflecting region that reflects a signal emitted by the sensing system, the one or more velocity values and the one or more coordinates obtained for the same instance of time, identifying that the set of the return points is associated with an object in an environment, and causing a driving path of the AV to be determined in view of the object.

Example embodiments described in this disclosure are generally directed to systems and methods for preventing vehicular mishaps. In an example method, an object detector mounted on a building or a roadside fixture, detects an object in a detection coverage area of the object detector. The object is undetectable by a collision avoidance system of a vehicle. The object detector conveys information about the object to a supervisory computer. The information can include, for example, a location and/or a direction of travel of the object (if the object is moving). The supervisory computer evaluates the information and transmits an alert to the collision avoidance system of the vehicle, so as to prevent a collision between the vehicle and the object. In an example situation, where the object is a pedestrian, the supervisory computer may also transmit a warning alert to a personal communication device of the pedestrian.

A method for generating a trigger signal for triggering at least one safety function of a motor vehicle. The method includes at least the following method steps: a) receiving respective signals from at least two pressure tube sensors, b) determining at least one collision parameter from the signals received according to step a), c) outputting the trigger signal for the at least one safety function as a function of the at least one collision parameter determined in step b).

The described aspects and implementations enable efficient calibration of a sensing system of a vehicle. In one implementation, disclosed is a method and a system to perform the method, the system including the sensing system configured to collect sensing data, characterizing an environment of the vehicle, the sensing data including infrared sensing data. The system further includes a data processing system operatively coupled to the sensing system and configured to process the sensing data using a classifier machine-learning model to obtain a classification of one or more vulnerable road users present in the environment of the vehicle.