A method and a device for operating an automated vehicle are provided. The method includes a step of receiving a first position of the automated vehicle, a step of receiving environment data values, the environment data values representing an environment of the automated vehicle, a step of detecting at least one further vehicle in the environment of the automated vehicle, and a step of generating a digital environment model, starting from a digital map, based on the environment data values and as a function of the first position of the automated vehicle. The environment model comprises the automated vehicle, the at least one further vehicle, and at least one simulated object in the environment of the automated vehicle. The method also includes a step of operating the automated vehicle as a function of the digital environment model.

Systems and methods for navigating a host vehicle are disclosed. In one implementation at least one processor is programmed to receive at least one image captured by a camera from an environment of the host vehicle; analyze the at least one image to identity a representation of a lane of travel of the host vehicle along a road segment and a representation of at least one additional lane of travel along the road segment; analyze the at least one image to identify an attribute associated with the at least one additional lane of travel; determine, based on the attribute, information indicative of a characterization of the at least one additional lane of travel; and send the information indicative of the characterization of the at least one additional lane of travel to a server for use in updating a road navigation model.

A system for controlling an autonomous vehicle is capable of predicting and eliminating a possibility of motion sickness before and during travelling of the autonomous vehicle. The system includes: a first control unit which compares a first vehicle motion sickness index that is determined by a travelling simulation with a first threshold set including passenger information before travelling and controls a boarding location of a passenger before travelling; and a second control unit which computes a passenger motion sickness index from passenger state information and vehicle state information detected during travelling, compares a second threshold indicating the degree of sensitivity to motion sickness according to passenger information during travelling with the passenger motion sickness index, and controls the autonomous vehicle so as to reduce or eliminate a generation of a motion sickness causing frequency.

Systems and methods are disclosed for identifying time-latency and subsystem control actuation dynamic delay due to second order dynamics that are neglected in control systems of the prior art. Embodiments identify time-latency and subsystem control actuation delays by developing a discrete-time dynamic model having parameters and estimating the parameters using a least-squares method over selected crowd-driving data. After estimating the model parameters, the model can be used to identify dynamic actuation delay metrics such as time-latency, rise time, settling time, overshoot, bandwidth, and resonant peak of the control subsystem. Control subsystems can include steering, braking, and throttling.

The disclosure relates to a method for planning a future trajectory of an autonomously or semi-autonomously driving vehicle, wherein sensor data are detected by means of at least one sensor of the vehicle, wherein an optimum trajectory for the vehicle is determined for an environmental status derived from the detected sensor data, wherein possible future trajectories of the vehicle are generated to this end and evaluated by means of a reward function, wherein in so doing, a behavior of the vehicle, a static environment and a behavior of other road users are taken into consideration, wherein an influence exerted by the behavior of the vehicle on the other road users is additionally taken into consideration in the reward function, and wherein the determined optimum trajectory is provided for execution.

An apparatus for post-processing of a decision-making model of an autonomous vehicle receives a decision-making model including a plurality of states. The model is processed using multivariate data that comprises values for at least three observations of a vehicle operational scenario. A slice of the model decision space is generated by fixing values of all except two observations, and modifying the values of the two observations to obtain multiple alternative solutions for the model. The alternative solutions and the modified values form the slice. Each alternative solution is associated with a respective first value of a first observation and a respective second value of a second observation. The apparatus also generates a solution to a modified decision-making model that is the model modified by, for at least one state and at least one of the two observations, modifying a probabilistic transition matrix, a probabilistic observation matrix, or both.

A traveling control apparatus is mounted on a vehicle that includes an electric motor and an internal combustion engine as power sources. The traveling control apparatus includes an electronic control unit configured to create a speed profile obtained by predicting speed of the vehicle at each time, derive, based on at least the speed profile, a coefficient profile that is a coefficient at each time used at the time of predicting an amount of regenerative energy recoverable by regenerative braking of the electric motor, approximate the speed profile with a predetermined approximation model and estimate a predicted amount of regenerative energy based on an approximation result and the coefficient profile, and determine the power source used for traveling based on the predicted amount of regenerative energy.

Techniques and methods associated with performing monitoring associated with operations of autonomous vehicles. For instance, the vehicle may capture sensor data using one or more sensors. The vehicle may then analyze the sensor data using systems in order to determine estimated locations associated with the vehicle and estimated locations associated with situations that may result in a potential unsafe scenario represented by the sensor data. Additionally, the vehicle may determine a distribution of estimated locations associated with the vehicle and using the distributions of estimated locations, the vehicle may determine risk probabilities associated with operations of the vehicle. The vehicle may then redefine or update a route or maneuver to perform when the risk probability exceeds a threshold.

Systems and methods can improve passenger safety for an Autonomous Vehicle (AV) based on the integration of sensor data captured by the AV's interior and exterior sensors. The AV can determine passenger occupancy data corresponding to where each passenger is detected within the AV by the interior sensors. The AV can determine multiple sets of one or more driving actions that the AV can perform at a future time. The AV can generate crash impact data corresponding to where each passenger is detected from one or more simulated collisions between the AV and one or more objected detected by the exterior sensors when the AV performs one or more sets of driving actions from among the multiple sets. The AV can determine ranked sets of driving actions based on the passenger occupancy data and the crash impact data.

Disclosed are systems and methods for responding to detected emergency vehicles. In some aspects, a method includes generating an emergency vehicle (EMV) detection signal based on sensor signals of an autonomous vehicle (AV); determining, based on the EMV detection signal, whether remote assistance (RA) is invoked for the EMV signal or whether the AV is to handle the EMV detection signal autonomously; and responsive to determining that the AV is to handle the EMV detection signal autonomously, determining a trajectory of the AV in response to the EMV detection signal.