Systems and methods for characterizing a driving style of an autonomous vehicle are presented. A system may include one or more sensors configured to collect information concerning driving characteristics; a memory containing computer-readable instructions for evaluating the driving characteristics for a pattern(s) correlatable with a driving style of the autonomous vehicle and for characterizing aspects of driving style based on the one or more patterns; and a processor configured to evaluate the driving characteristics for the one or more patterns correlatable with the driving style, and characterize aspects of the driving style based on the pattern(s). Corresponding methods and non-transitory media are disclosed.

Systems and methods for controlling a vehicle are described. A processor of a longitudinal planning system determines a state of the vehicle. The processor determines a state of a leader vehicle. The processor, based on the determined state of the vehicle and the determined state of the leader vehicle, determines a critical distance for the vehicle. The processor compares a distance between the vehicle and the leader vehicle with the critical distance. The processor, based on the comparison, determines whether the vehicle is too close to or too far from the leader vehicle. The processor, based on the determination, applies one or more of overshoot constraints, undershoot constraints, and critical constraints. After applying the one or more of overshoot constraints, undershoot constraints, and critical constraints, the processor determines a target acceleration for the vehicle. The processor controls the vehicle to track the target acceleration for the vehicle.

A method for controlling an approach of a traveling vehicle to at least one preceding reference vehicle. The method includes using an automated distance setting between the vehicle and the reference vehicle, at an acceleration that may be applied for the vehicle and that is a function of an operating position of a control element of the vehicle that is actuatable by the driver of the vehicle, and that is associated with a temporal acceleration profile for the automated distance setting. A maximum highest acceleration value of the acceleration profile implementing the automated distance setting is specified as a function of the operating position. A distance controller, a computer program, and a memory unit are also described.

The disclosure relates to a method for evaluating a driving behavior, wherein detected driving data of at least one human driver, or detected driving data of at least one automated driving vehicle are obtained, wherein a key performance indicator is determined based on the obtained driving data, wherein both a travel time as well as an energy efficiency and/or emissions efficiency are taken into account in determining the key performance indicator, wherein the determined key performance indicator is provided as an evaluation result.

A slope estimation device estimates a slope of a vehicle traveling road, and includes an input section that acquires a detected value of an acceleration sensor for detecting acceleration in a front-back direction of the vehicle, a centripetal force detecting section that detects centripetal force acting on the acceleration sensor due to a turning motion of the vehicle, and a slope computing section that computes the slope of the vehicle traveling road based on the detected value of the acceleration sensor. When the vehicle is in the turning motion, the slope computing section computes the slope of the traveling road by determining a component of the centripetal force superimposed on the detected value of the acceleration sensor based on a turning center position of the vehicle, a gravity center position of the vehicle, and an installation position of acceleration sensor, and subtracting the component of the centripetal force from the detected value of the acceleration sensor.

In general, techniques are described by which a computing system detects low-impact collisions. A computing system includes at least one processor and memory. The memory includes instructions that, when executed, cause the at least one processor to determine whether an object collided with a vehicle based on a comparison of data received from at least one motion sensor configured to measure at least an acceleration of the vehicle and data received from a plurality of level sensors, wherein each level sensor is configured to measure a relative position between a body of the vehicle and a respective wheel of a plurality of wheels of the vehicle. Execution of the instructions further causes the at least one processor to perform one or more actions in response to determining that the object collided with the vehicle.

Methods and systems implemented in a vehicle involve obtaining a single camera image from a camera arranged on the vehicle. The image indicates a heading angle ψ.sub.0 between a vehicle heading x and a tangent line that is tangential to road curvature of a road on which the vehicle is traveling and also indicates a perpendicular distance y.sub.0 from a center of the vehicle to the tangent line. An exemplary method includes obtaining two or more inputs from two or more vehicle sensors, and estimating kinematic states of the vehicle based on applying a Kalman filter to the single camera image and the two or more inputs to solve kinematic equations. The kinematic states include roll angle and pitch angle of the vehicle.

A predictive driver intention to stop (DITS) system for a vehicle having an engine includes one or more sensors configured to measure a set of operating parameters of the vehicle including at least (i) vehicle speed and (ii) vehicle deceleration rate. A controller is configured to identify no-stop braking events and complete stop braking events, and reference a generated baseline probability table indicating a probability of a driver braking to bring the vehicle to a stop, based on at least the vehicle speed and vehicle deceleration rate measured during at least one of the identified no-stop braking events and complete stop braking events. The controller is further configured to predict a DITS event based on the generated baseline probability table, and control operation of the engine based on the predicted DITS event to facilitate reducing vehicle fuel consumption and/or tailpipe emissions.

A computer-implemented method of determining a series of control signals for controlling an autonomous vehicle to implement a planned speed change maneuver comprises: receiving from a maneuver planner a position target for the planned speed change maneuver; selecting, from a predetermined family of kinematic functions, a kinematic function for carrying out the planned speed change maneuver, each kinematic function being a first or higher order derivative of acceleration with respect to time; and using the selected kinematic function to determine a series of control signals for implementing the planned speed change maneuver; wherein the kinematic function is selected in a constrained optimization process as substantially optimizing a cost function defined for the speed change maneuver, subject to a set of hard constraints that: (i) require a final acceleration, speed and position corresponding to the selected kinematic function to satisfy, respectively, an acceleration target, a speed target and the position target, given an initial speed and acceleration of the autonomous vehicle, and (ii) impose a jerk magnitude upper limit on the selected kinematic function.

The disclosure belongs to the technical field of autonomous vehicle, in particular to an automatic driving acceleration test method considering efficiency and coverage, which includes the following steps. Step 1 is definition of scenario test priority. Step 2 is zone division. Step 3 is search within zones. Step 4 is update of scenario test priorities. Step 5 is iterative test. After selecting the automatic driving function to be tested and setting the parameters of the vehicle operation zone, the scenario generation range is formed. The coverage of the test scenario is improved by dividing the generated range and setting the freedom of early autonomous driving exploration. The efficiency of the test process is improved by continuously improving the probability of generating dangerous scenarios in the test process. Thus, it is ensured that the generated test scenarios take into account both test efficiency and test coverage.