This disclosure provides systems and methods for path planning by a planner of an autonomous vehicle. The method may include receiving by the planner perception data from a perception module, wherein the perception data comprises tracking or predicted object data associated with objects or obstacles in an environment of the autonomous vehicle, and wherein the tracking or predicted object data are determined based on high recall detection data and high precision detection data, generating by the planner a trajectory for controlling the autonomous vehicle based on the perception data received from the perception module, and transmitting to a controller of the autonomous vehicle the trajectory such that the autonomous vehicle is navigated by the controller to a destination.

System, methods, and other embodiments described herein relate to implementing and calibrating a learning model for inferring operator intent by estimating grip intensity. In one embodiment, a method includes estimating, using a learning model during a driving scenario, first grip intensity on a steering device for a vehicle according to initial image data depicting a hand of an operator gripping outside set areas that have pressure sensors. The method also includes calibrating the learning model for the operator and the steering device using grip measurements and additional image data acquired from gripping inside the set areas. The method also includes computing, using the learning model during the driving scenario, second grip intensity outside the set areas on the steering device according to hand images acquired about the operator. The method also includes adapting a vehicle parameter of the vehicle according to the second grip intensity.

The present disclosure generally relates to sensor calibration and more specifically, to sensor calibration using weather data. In some aspects, the present disclosure provides a process for receiving a set of weather data from a weather measurement station disposed within a calibration environment, determining if a predetermined weather condition is indicated by the set of weather data, and collecting, using one or more AV sensors, a set of sensor data within the calibration environment, if the predetermined weather condition is indicated by the set of weather data. Systems and machine-readable media are also provided.

A live map system may be used to propagate observations collected by autonomous vehicles operating in an environment to other autonomous vehicles and thereby supplement a digital map used in the control of the autonomous vehicles. In addition, a live map system in some instances may be used to propagate location-based teleassist triggers to autonomous vehicles operating within an environment. A location-based teleassist trigger may be generated, for example, in association with a teleassist session conducted between an autonomous vehicle and a remote teleassist system proximate a particular location, and may be used to automatically trigger a teleassist session for another autonomous vehicle proximate that location and/or to propagate a suggested action to that other autonomous vehicle.

According to one embodiment, a method, computer system, and computer program product for navigating an autonomous vehicle is provided. The present invention may include measuring, in real time, movement patterns of air in the surroundings of the autonomous vehicle; analyzing the movement patterns to identify wind flow patterns, terrain, and passage in the surroundings; and navigating the autonomous vehicle based on the identified wind flow patterns, terrain, and passage.

A first off-network mission critical (ONMC) wireless device discovers ONMC wireless devices employing a proximity services discovery procedure. First media traffic is transmitted employing a first mission critical session to a third ONMC wireless device. A message comprising an organization field identifier and an organization identifier is received from a second ONMC wireless device. A call priority is determined, at least in part, based on the organization field identifier and the organization identifier. Transmission of the first media traffic terminates, based at least on the call priority. Transmission of second media traffic to the second ONMC wireless device employing a second mission critical session may be started.

Provided herein is a system for determining whether hands-free driving operation should be activated. The system is implemented in conjunction with a vehicle, and the system includes a control unit. The control unit is configured to: retrieve historical data associated with hands-free operation of the vehicle, generate, based upon the historical data, an operational map, receive, from the vehicle, current vehicle data, determine, based upon the operational map and the current vehicle data, that hands-free operation is available, and notify the driver that the hands-free operation is recommended.

A method and device for operating an automated vehicle, including: acquiring an environment of the automated vehicle including the traffic intersection; preparing an environment map based on the acquired environment, the environment map including a subdivision into grid cells, each including occupancy probabilities and velocity distributions; acquiring an updated environment of the automated vehicle; adapting the occupancy probabilities and the velocity distributions for each grid cell; determining the occupancy probabilities and velocity distributions of an expected environment in a next time step; repeatedly executing the steps until a final occupancy probability and a final velocity distribution is determined for each grid cell according to predefined criteria; determining a driving strategy for the automated vehicle as a function of the final occupancy probabilities and the final velocity distributions; and operating the automated vehicle as a function of the driving strategy.

A driver assistance apparatus for a vehicle calculates a vehicle speed of a random vehicle present ahead of the vehicle, and momentum of a front part or a rear part of the random vehicle, on the basis of detection data by a surrounding environment sensor configured to detect data regarding surrounding environment around the vehicle, and estimate a slip angle or a slip angular speed of a rear wheel of the random vehicle, on the basis of data regarding at least the vehicle speed of the random vehicle and the momentum of the front part or the rear part of the random vehicle, and data regarding an estimated distance from the rear wheel of the random vehicle to the front part or the rear part of the random vehicle.

Systems and methods are provided herein for an AV to more efficiently, quickly, and accurately determine a likelihood of collision with another object, for example, by recursive triangulation. A candidate trajectory of the AV is then selected for controlling the AV based on the determined likelihood(s) of collision. According to one example, the likelihood of collision is determined by: 1) recursively triangulating a convex hull that defines all of the locations in which a collision would occur between the object and the AV (e.g., in a manner similar to the Sierpi?ski method), and 2) multiplying the area of each resulting triangular region by the value of a probability density that defines all of the locations in which a collision would occur between the object and the AV function in that region.