The disclosure relates to a scintillator material for a radiation detector. In an embodiment, the scintillator material can include a crystalline alkaline-earth metal halide comprising at least one alkaline-earth metal selected from Mg, Ca, Sr, Ba, said alkaline-earth metal halide being doped with at least one dopant that activates the scintillation thereof other than Sm.sup.2+, and co-doped with Sm.sup.2+, said alkaline-earth metal halide comprising at least one halogen selected from Br, Cl, I.

A damping control device for an electric vehicle including a motor and a transmission in a drive system between the motor and a drive wheel includes a detector, a bandpass filter, first to third calculators, and a controller. The detector detects a rotation speed of the motor. The bandpass filter passes a vibration component included in the detected motor rotation speed, in a resonance frequency band of the drive system. The first calculator calculates a damping torque for damping resonance of the drive system with a motor torque, based on the passed vibration component. The second calculator calculates, as a damping torque offset value, an average value of the calculated damping torque for a predetermined time. The third calculator calculates a target damping torque. The controller controls a drive state of the motor.

A method for predicting a trajectory of a target vehicle in an environment of a vehicle. The method includes the steps of a) capturing states of the target vehicle, capturing states of further vehicle objects in the environment of the vehicle and capturing road markings by a camera-based capture device; b) preprocessing the data obtained in step a), wherein outliers are removed and missing states are calculated; c) calculating an estimated trajectory by a physical model on the basis of the data preprocessed in step b); d) calculating a driver-behavior-based trajectory on the basis of the data preprocessed in step b); and e) combining the trajectories calculated in steps c) and d) to form a predicted trajectory of the target vehicle.

A vehicle outputs a driving sound generated based on the driver's preference rather than outputting a uniform driving sound. The vehicle includes a sensor that acquires at least one of a facial image, an audio signal, and a biometric signal. A database stores a plurality of sound sources classified according to driver information. A controller calculates the driver information based on at least one of the facial image, the audio signal, and the biometric signal, selects any one of the plurality of sound sources stored in the database based on the calculated driver information, generates a driving sound based on the selected sound source, and operates a speaker to output the generated driving sound.

Aspects of the disclosure relate to generating simulated degraded sensor data. For instance, first sensor data collected by a sensor of a perception system of an autonomous vehicle may be received. The first sensor data may be inputted into simulated degraded sensor data for a particular degrading condition. The simulated degraded sensor data may be used to evaluate or train a model for detecting objects of the perception system.

A system for controlling a vehicle by jointly estimating a state of a vehicle and a function of a tire friction of a vehicle traveling on a road uses a particle filter maintaining a set of particles. Each particle includes an estimation of a state of the vehicle, an estimation of probability density function (pdf) of the tire friction function, and a weight indicative of a probability of the particle. The system executes the particle filter to update the particles based on a motion model and a measurement model of the vehicle, control commands moving the vehicle and measurements of the state where the vehicle moved according to the control commands. A control command is generated based on the motion of the vehicle, the weighted combinations of the state of the vehicle and the pdf of the tire friction function weighted according corresponding weights of the particles.

The technology relates to autonomous vehicles having articulating sections such as the trailer of a tractor-trailer. Aspects include approaches for tracking the pose of the trailer, including its orientation relative to the tractor unit. Sensor data is analyzed from one or more onboard sensors to identify and track the pose. The pose information is usable by on-board perception and/or planning systems when driving the vehicle in an autonomous mode. By way of example, on-board sensors such as Lidar sensors are used to detect the real-time pose of the trailer based on Lidar point cloud data. The orientation of the trailer is estimated based on the point cloud data, and the pose is determined according to the orientation and other information about the trailer. Aspects also include determining which side of the trailer the sensor data is coming from. A camera may also detect trailer marking information to supplement the analysis.

A yaw motion control method for a four-wheel distributed vehicle includes: calculating the steering response of the vehicle in a steady state using a nonlinear vehicle model in reference with an understeering degree while constraining by the limit value of the road surface adhesion condition according to the sideslip angle response and the vertical load change in the steady state, calculating the lateral force response and the self-aligning moment response of the tires in the steady state by a magic tire formula, calculating the required additional yaw moment by using the yaw motion balance equation, reasonably distributing the generalized control force to the four drive motors through the optimization algorithm in combination with the current driving conditions; finally, off-line storing and retrieving the calculation results of the off-line distribution of different vehicle parameters required by different upper layers to distribute the torques to the four drive wheels.

Aspects of the disclosure provide for generation of trajectories for a vehicle driving in an autonomous driving mode. In one instance, a default number of trajectories to be generated may be identified. A set of maneuvering options may be selected from a set of predetermined maneuvering options based on the number of trajectories. The set of maneuvering options may be filtered based on the default number of trajectories. A set of trajectories may be generated based on the filtered set of maneuvering option such that each trajectory of the set corresponds to a different maneuvering behavior. A cost for each trajectory of the set of trajectories may be determined, and one of the trajectories of the set of trajectories may be selected based on the determined costs. The vehicle may be maneuvered in the autonomous driving mode according to the selected one of the trajectories.

Disclosed is a method and apparatus for detecting a state of a vehicle occupant, wherein a bio-signal of an occupant riding in a vehicle may be acquired by executing an artificial intelligence (AI) algorithm or a machine learning algorithm, and a physical change of the occupant may be measured by a movement signal, a respiratory signal, and a heart rate signal from the acquired bio-signal of the vehicle occupant, such that it is possible to control an operation of an internal vehicle device or to control operation of the vehicle so as to correspond to the physical change of the occupant estimated by communicating with the internal vehicle device in a 5G communication environment. According to the present disclosure, the movement signal, respiratory signal, and heart rate signal of the occupant in the vehicle may be extracted via an RF sensor mounted in the vehicle, and the physical state of the occupant may be estimated based on the extracted bio-signal of the occupant, and when the physical state is estimated to be abnormal, the occupant may be informed of the abnormal physical state.