Systems and methods are provided for autonomous vehicle navigation. The systems and methods may map a lane mark, may map a directional arrow, selectively harvest road information based on data quality, map road segment free spaces, map traffic lights and determine traffic light relevancy, and map traffic lights and associated traffic light cycle times.

A vehicle and associated method for calculating tire saturation is provided. The method may include the stability control computer calculating slip ratio and longitudinal force for the tire, calculating tire longitudinal stiffness by dividing longitudinal force by slip ratio, calculating tire saturation from tire longitudinal stiffness, and the stability control computer altering dynamic control of the vehicle based on calculated tire saturation. The stability control computer may calculate tire saturation from a tire saturation membership function which includes a first tire longitudinal stiffness value representing an unsaturated tire, a second tire longitudinal stiffness value representing a saturated tire, and a function line connecting the first tire longitudinal stiffness value to the second tire longitudinal stiffness value.

Example embodiments relate to a method for cut-in identification and classification. An example embodiment includes a obtaining operational data about one or more vehicles; based on the operational data, identifying the presence of one or more cut-ins within the operational data; extracting, from the operational data, cut-in data that depicts one or more of the cut-ins identified within the operational data; and, based on the extracted cut-in data, training a model for controlling an autonomous vehicle. Identifying the presence of a given cut-in includes: determining that at least one vertex of a bounding box surrounding a vehicle was located more than a threshold distance within a lane being navigated by a given vehicle; and determining that the ability of the given vehicle to maintain its course and speed was impeded by the presence of the particular additional vehicle within the lane.

Disclosed herein are systems and method including a method for managing an autonomous vehicle. The method include providing as first input to a machine learning model a raster image and a vector associated with a context of a scene comprising an autonomous vehicle and a plurality of agents, providing as second input to the machine learning model a planned travel path for the autonomous vehicle, based the first input and the second input, outputting from the machine learning model a plurality of yield/assert predictions, wherein the plurality of yield/assert predictions comprises a respective yield/assert prediction related to whether to yield or to assert in relation to each respective agent of the plurality of agents and causing the autonomous vehicle to travel along the planned travel path while yielding or asserting against the plurality of agents according to the plurality of yield/assert predictions.

Embodiments of the present disclosure relate to the technical field of autonomous driving, and in particular to methods for updating an autonomous driving system, autonomous driving systems, and on-board apparatuses. In the embodiments of the present disclosure, the autonomous driving system, in a manual driving mode, senses the surrounding environment of a vehicle, performs vehicle positioning, and plans a path for autonomous driving for the vehicle. However, the autonomous driving system does not issue an instruction to control the driving of the vehicle. Instead, it compares the path with a path along which a driver drives the vehicle in the manual driving mode to update a planning and control algorithm of the autonomous driving system. As such, the updated autonomous driving system better caters to the driving habits of the driver and improves the driving experience for the driver without compromising the reliability of planning and decision-making of autonomous driving.

The disclosure is related to a method and a system for assisting a driver of a road vehicle. A data processing unit receives data representing at least a relative position of the road vehicle, outputs, through a display device and/or an interior lighting device, a visual alert when the data processing unit determines that the data fulfill a first condition, and outputs, through a sound-emitting device, an audible alert when the data processing unit determines that the data fulfill a second condition after fulfilling the first condition.

A vehicle positioning method via data fusion and a system using the same are disclosed. The method is performed in a processor electrically connected to a self-driving-vehicle controller and multiple electronic systems. The method is to perform a delay correction according to a first real-time coordinate, a second real-time coordinate, real-time lane recognition data, multiple vehicle dynamic parameters, and multiple vehicle information received from the multiple electronic systems with their weigh values, to generate a fusion positioning coordinate, and to determine confidence indexes. Then, the method is to output the first real-time coordinate, the second real-time coordinate, and the real-time lane recognition data that are processed by the delay correction, the fusion positioning coordinate, and the confidence indexes to the self-driving-vehicle controller for a self-driving operation.

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.

This disclosure is directed to techniques for identifying relevant objects within an environment. For instance, a vehicle may use sensor data to determine a candidate trajectory associated with the vehicle and a predicted trajectory associated with an object. The vehicle may then use the candidate trajectory and the predicted trajectory to determine an interaction between the vehicle and the object. Based on the interaction, the vehicle may determine a time difference between when the vehicle is predicted to arrive at a location and when the object is predicted to arrive at the location. The vehicle may then determine a relevance score associated with the object using the time difference. Additionally, the vehicle may determine whether to input object data associated with the object into a planner component based on the relevance score. The planner component determines one or more actions for the vehicle to perform.

In a method for automated avoidance of a collision of a vehicle with an object in the surroundings of the vehicle, multiple vehicle paths are predicted and each one is weighted with a vehicle path probability, the vehicle surroundings are recorded with an imaging vehicle sensor, an object in the vehicle surroundings is captured, at least one object path in the vehicle surroundings is predicted and is weighted with an object path probability, one of the vehicle paths is tested for collision with the at least one object path and if a collision is possible, a collision probability with the at least one object path is calculated, a weighting criterion for an overall collision probability of the vehicle with the object is ascertained and a test is performed of whether the weighting criterion exceeds a threshold and if the threshold is exceeded a collision avoidance maneuver is triggered.