The technology relates to determining general weather conditions affecting the roadway around a vehicle, and how such conditions may impact driving and route planning for the vehicle when operating in an autonomous mode. For instance, the on-board sensor system may detect whether the road is generally icy as opposed to a small ice patch on a specific portion of the road surface. The system may also evaluate specific driving actions taken by the vehicle and/or other nearby vehicles. Based on such information, the vehicle's control system is able to use the resultant information to select an appropriate braking level or braking strategy. As a result, the system can detect and respond to different levels of adverse weather conditions. The on-board computer system may share road condition information with nearby vehicles and with remote assistance, so that it may be employed with broader fleet planning operations.

An operating system for an automated vehicle includes a failure-detector and a controller. The failure-detector detects a component-failure on a host-vehicle. Examples of the component-failure include a flat-tire and engine trouble that reduces engine-power. The controller operates the host-vehicle based on a dynamic-model. The dynamic-model is varied based on the component-failure detected by the failure-detector.

Method and apparatus for wheel speed estimation include an electrical powertrain having an electric motor providing a motor speed, a wheel, and a mechanical coupling between the motor and the wheel, and an electronic control unit calculating an estimated wheel speed based on the motor speed and mechanical dynamic models of the electrical powertrain.

A method, system and robot, wherein the robot includes two or more sensing devices, sensor A and sensor B, mounted thereon and moves forward along an axis parallel to the rows of plants, being autonomously steered by exerting angular corrections to place the robot as close as possible to the centerline between the rows of plants, wherein the method and system includes the following: (ii) dividing a two-dimensional grid of square cells into groups of cells; (iii) obtaining data points using sensor A and sensor B; (vii) moving the robot: (a) by turning right; or (b) by turning left; or (c) forward without turning,
depending on whether each group of cells is calculated as low-activated, high-activated or not activated using the data points.

A method may include obtaining input information that describes a driving operation of a vehicle and obtaining a rule that indicates an approved driving operation of the vehicle. The method may include parsing the rule using a domain-specific language to generate rule conditions in which the domain-specific language is a programming language that is specifically designed for analyzing driving operations of vehicles. The method may include representing the input information as observations relating to the vehicle in which each of the observations is comparable to one or more of the rules. The method may include comparing the observations to one or more respective comparable rule conditions and generating a grading summary that evaluates how well the observations satisfy the respective comparable rule conditions based on the comparison. A future driving operation of the vehicle may be adjusted based on the grading summary.

A vehicle includes a powertrain, an inertial measurement unit configured to measure inertial forces exerted onto the vehicle, and a controller. The controller is programmed to control the torque at the powertrain based on a mapped relationship between the inertial forces and a vehicle velocity, wherein the mapped relationship utilizes at least one mapping parameter. The controller is further programmed to estimate a mass of the vehicle based on the mapping parameter.

A method for determining the values of parameters for a controller of a vehicle, wherein respective error measures are calculated for different sets of values and a set of values is selected based on the error measures.

A method for predicting a trajectory of at least one secondary road user for avoiding a collision course with the secondary road user for a host vehicle. The method includes determining the present location for the host vehicle, retrieving a plurality of modelled clusters of trajectories for a present traffic situation, and detecting the position and speed of the at least one secondary road user. The method also includes predicting at least one feasible trajectory for the at least one secondary road user based on the position and the speed of the at least one secondary road user to the plurality of modelled clusters of trajectories and selecting at least one feasible trajectory of the feasible trajectories for each secondary road user based on a selection criterion. At least one action is performed based on the selected at least one feasible trajectory.

A safety system for a vehicle may include one or more processors configured to determine, based on a friction prediction model, one or more predictive friction coefficients between the ground and one or more tires of the ground vehicle using first ground condition data and second ground condition data. The first ground condition data represent conditions of the ground at or near the position of the ground vehicle, and the second ground condition data represent conditions of the ground in front of the ground vehicle with respect to a driving direction of the ground vehicle. The one or more processors are further configured to determine driving conditions of the ground vehicle using the determined one or more predictive friction coefficients.

In one embodiment, a control command is generated with an MPC controller, the MPC controller including a cost function with weights associated with cost terms of the cost function. The control command is applied to a dynamic model of an autonomous driving vehicle (ADV) to simulate behavior of the ADV. One or more of the weights are based on evaluation of the dynamic model in response to the control command, resulting in an adjusted cost function of the MPC controller. Another control command is generated with the MPC controller having the adjusted cost function. This second control command can be used to effect movement of the ADV.