The present invention relates to a method for estimating an accident risk of an autonomous driving unit. The method produces helpful results with less autonomous driving cycles. An autonomous-driving quantity, quantifying an autonomous-driving quality of the driving of the driving unit, is determined from driving values that have been determined from monitoring at least one driving parameter of the driving unit during autonomous driving. The autonomous-driving quantity is associated with a plurality of manual-driving quantities that have been determined from the same driving parameter during manual driving periods of different driving units. An autonomous-driving accident rate value is determined from accident rate values associated with those manual-driving quantities.

A computer-implemented method is provided for redundancy reduction for driving test scenarios. The method includes receiving an original test set of driving scenarios and a driving model which simulates a vehicle behavior under a driving scenario inputted to the driving model. The method includes, for each driving scenario of the original test set, obtaining vehicle dynamics timeseries data as an output of the driving model. The method includes determining similar driving scenarios by comparing driving model outputs. The method additionally includes creating a new test set of driving scenarios by discarding duplicated ones of the similar driving scenarios from the original test set.

The present disclosure describes a method for monitoring operations of an automated driving system (ADS) of a vehicle. For each monitored operation the method includes: determining a geographical position of the vehicle; determining an intended path of the vehicle; and determining one or more intended parameters associated with performing a driving manoeuvre of said vehicle from the determined geographical position along the intended path. For each monitored operation the method further includes: obtaining one or more parameters associated with performing the driving manoeuvre of said vehicle from said determined geographical position; and retrieving, from a statistical model, data indicative of a statistical distribution related to one or more corresponding intended and/or obtained parameters for said intended path. Based on said retrieved data, determining whether there is an anomaly associated with said monitored operation; and taking at least one action of a set of predefined actions if an anomaly is determined.

The present disclosure is directed to systems and methods directed to improving the functions of a vehicle. Systems and methods are provided that provide a custom tool that autogenerates a set of software agents that allows a system to separate processing, transmission and receiving of messages to achieve better synchronization. The disclosure herein also provides a simplified method of key provisioning by designating one client as a server and assigning a symmetric key to every other client permanently provisioned between that client and the server. Systems and method are further provided that predict faults in a vehicle. Systems and methods are also provided that preserve data in the event of a system crash. Systems and methods are also provided in which an operating system of a vehicle detects the presence of a new peripheral and pulls the related interface file for that new peripheral. Further, a data synchronization solution is provided herein which provides optimized levels of synchronization.

A system calibrates a function of a tire friction of a vehicle traveling on a road from motion data including a sequence of control inputs to the vehicle that moves the vehicle on the road and a corresponding sequence of measurements of the motion of the vehicle moved by the sequence of control inputs. The system updates iteratively the probability distribution of the tire friction function until a termination condition is met, wherein, for an iteration, the system samples the probability distribution of the tire friction function, determines a state trajectory of the vehicle to fit the sequence measurements according to the measurement model and the sequence of control inputs according to the motion model including the sample of the tire friction function, and updates the probability distribution of the tire friction function based on the state trajectory of the vehicle.

Included are: a simulation data acquiring unit to acquire simulation sensor data and acquire simulation traveling data; a feature amount calculating unit to calculate a feature amount from the simulation sensor data; a hyperparameter evaluation unit to evaluate whether or not a hyperparameter is a determined hyperparameter by comparing the simulation traveling data with ideal traveling data; a hyperparameter determination control unit to reset the hyperparameter until the hyperparameter evaluation unit evaluates that the hyperparameter is the determined hyperparameter, and repeatedly operate a mobile object simulator; and a teacher data generating unit to generate teacher data in which the hyperparameter evaluated as the determined hyperparameter by the hyperparameter evaluation unit and the feature amount calculated by the feature amount calculating unit are paired.

A safety system (200) for a vehicle (100) is provided. The safety system (200) may include one or more processors (102). The one or more processors (102) may be configured to control a vehicle (100) to operate in accordance with the predefined stored driving model parameters, to detect vehicle operation data during the operation of the vehicle (100), to determine whether to change predefined driving model parameters based on the detected vehicle operation data and the driving model parameters, to change the driving model parameters to changed driving model parameters, and to control the vehicle (100) to operate in accordance with the changed driving model parameters.

The present disclosure relates to driving decision-making methods, apparatuses, and chips. One example method includes building a Monte Carlo tree based on a current driving environment state, where the Monte Carlo tree includes a root node and N-1 non-root nodes, each node represents one driving environment state, and a driving environment state represented by any non-root node is predicted by a stochastic model of driving environments. Based on at least one of an access count or a value function of each node in the Monte Carlo tree, a node sequence that starts from the root node and ends at a leaf node is determined, and a driving action sequence is determined based on a driving action corresponding to each node in the node sequence.

There is provided mechanisms for AD, or at least ADAS, aided manoeuvring of a vehicle. A method includes selecting between using a CRLS based positioning system and a GNSS based positioning system for aiding manoeuvring of the vehicle. Which positioning system to select is based on comparing a first error determined for the CRLS based positioning system to a second error determined for the GNSS based positioning system. The positioning system with smallest error is selected. The method comprises aiding the manoeuvring of the vehicle using the selected positioning system. The manoeuvring pertains to reversing of the vehicle.

This document discloses system, method, and computer program product embodiments for operating a vehicle, comprising: using kinematic models to generate forecasted trajectories of an actor (the kinematic models being respectively associated with different actor types that are assigned to an actor detected in an environment of the vehicle); selecting a first kinematic model based on the forecasted trajectories and a kinematic state of the actor; using the first kinematic model to predict a first path for the actor; selecting a second kinematic model responsive to movement of the actor no longer being consistent with typical movement of an object of one of the different actor types that is associated with the first kinematic model; using the second kinematic model to predict a second path for the actor; and controlling operations of the vehicle based on the first and second paths.