A driver assistance system for automated longitudinal guidance of a motor vehicle configured to detect at least one turning opportunity for the motor vehicle, and to reduce the speed of the motor vehicle in accordance with the at least one detected turning opportunity.

A method of multi-sensor data fusion includes determining a plurality of first data sets using a plurality of sensors, each of the first data sets being associated with a respective one of a plurality of sensor coordinate systems, and each of the sensor coordinate systems being defined in dependence of a respective one of a plurality of mounting positions for the sensors; transforming the first data sets into a plurality of second data sets using a transformation rule, each of the second data sets being associated with a unified coordinate system, the unified coordinate system being defined in dependence of at least one predetermined reference point; and determining at least one fused data set by fusing the second data sets.

A method, apparatus, and system are described. The method includes generating a set of current values associated with at least one component included on a moving vehicle and providing the set of current values over a wireless network. The values are generated by one or more sensors. An edge computing device receives the current values. The method further includes processing the set of current values in real-time using at least one machine learning algorithm to identify a value of a point in time for a failure of one of the at least one component based on the set of current values and at least one set of past values received. The past values are stored in a memory. The set of current values are transmitted with a low time latency between the generating the set of current values and the processing of the set of current values.

Methods and devices for operating a Self-Driving Car (SDC) are disclosed. The method includes generating a first graph-structure having nodes and edges, ranking the edges based on a priority logic into a ranked list of edges, and generating a second graph-structure (i) by iteratively generating attributes for respective ones from the ranked list of edges beginning with a highest priority edge in the ranked list of edges and (ii) until a pre-determined limit is met. The method also includes causing operation of the SDC on the road segment using the second graph-structure.

A method using unsupervised velocity prediction and correction for urban driving from sequences of noisy position estimates includes: performing a vehicle velocity prediction for one or more other vehicles in a vicinity of a host automobile vehicle; calculating a first heuristic based on a uniformity test; calculating a second heuristic based on a vehicle speed of the one or more other vehicles; combining the first heuristic and the second heuristic using a weighted sum; determining an uncertainty mask applying the combined first heuristic and the second heuristic and a heuristic threshold; and applying the uncertainty mask to identify a velocity correction for use by the host automobile vehicle.

Provided is a system and method that can control a merge of an autonomous vehicle when other vehicles are present on the road. In one example, the method may include iteratively estimating a series of values associated with one or more vehicles in an adjacent lane with respect to an ego vehicle, identifying a trend associated with the one or more vehicles from the iteratively estimated series of values, determining merge intentions of the one or more vehicles with respect to the ego vehicle based on the identified trend over time, verifying the merge intentions against a simulated change in the trend, selecting a merge position of the ego vehicle with respect to the one or more vehicles within the lane based on the verified merge intentions, and executing an instruction to cause the ego vehicle to perform a merge operation based on the selected merge position.

Provided are autonomous vehicles (AV), computer program products, and methods for maneuvering an AV in a roadway, including receiving forecast information associated with predicted trajectories of one or more actors in a roadway, determining a relevant trajectory of an actor based on correlating a forecast for predicted trajectories of the actor with the trajectory of the AV, regenerate a distance table for the relevant trajectory previously generated for processing constraints, generate a plurality of margins for the AV to evaluate, the margins based on a plurality of margin types for providing information about risks and effects on passenger comfort associated with a future proximity of the AV to the actor, classifying an interaction between the AV and the actor based on a plurality of margins, and generating continuous scores for each candidate trajectory that is also within the margin of the actor generated for the relevant trajectory.

Provided is a method for controlling movement of an autonomous mobile machine. The method includes that: a target path and a current state of the autonomous mobile machine are acquired; at least one preview distance is calculated according to a current speed; at least one preview point each corresponding to a respective one of the at least one preview distance is acquired according to the target path and the at least one preview distance; a lateral deviation from each preview point to a current position is calculated; a direction control angle parameter of a current control period is acquired according to the lateral deviation, the current speed and a preset parameter matching table; and the autonomous mobile machine is controlled to move according to the direction control angle parameter. Also provided are an apparatus for controlling movement of an autonomous mobile machine, a machine and a storage medium.

A computer includes a processor and a memory storing instructions executable by the processor to receive a series of sample coordinate points of a projected path of travel of a vehicle, generate interpolated coordinate points along the projected path between the sample coordinate points, fit a curve to the sample coordinate points and interpolated coordinate points, and output a curvature of a lane at a reported coordinate point along the projected path based on the curve.

Techniques are described herein for generating trajectories for autonomous vehicles using velocity-based steering limits. A planning component of an autonomous vehicle can receive steering limits determined based on safety requirements and/or kinematic models of the vehicle. Discontinuous and discrete steering limit values may be converted into a continuous steering limit function for use during on-vehicle trajectory generation and/or optimization operations. When the vehicle is traversing a driving environment, the planning component may use steering limit functions to determine a set of situation-specific steering limits associated with the particular vehicle state and/or driving conditions. The planning component may execute loss functions, including steering angle and/or steering rate costs, to determine a vehicle trajectory based on the steering limits applicable to the current vehicle state.