Abstract: The invention relates to a system and method for navigating an autonomous driving vehicle (ADV) that utilizes ADV an-onboard computer and/or one or more ADV control system nodes in an ADV network platform. The on-board computer receives sensor data concerning one or more occupants occupying an ADV and/or the ADV itself and, upon a detection of an event, automatically initiates a dynamic routing algorithm that utilizes artificial intelligence to re-route the ADV to another destination, for example a healthcare facility. One or more embodiment of the system and method include an ADV on-board computer and/or one or more ADV network platform nodes that utilize in-memory processing to aid in the generation of routing, health and navigational information to advantageously navigate an ADV.

Abstract: A falling object determination device configured to be mounted on a vehicle is provided. The falling object determination device includes an acquisition unit configured to acquire, as a first detection signal, information indicating displacement of an object near a cargo bed of a nearby vehicle which travels near the own vehicle; and a control unit configured to calculate at least one of a vibration frequency, an amplitude and a size of the object near the cargo bed by using the first detection signal, and performs a determination whether the object near the cargo bed is an object which is likely to fall from the cargo bed or an object which is falling from the cargo bed in accordance with a calculation result.

Abstract: The disclosure discloses a method and an apparatus for simulating a vehicle. The detailed implementation includes: determining, by a tire dynamics module, a tire translational force at a current moment based on a control instruction issued by an upper planning module and motion data of a vehicle body and a tire normal load outputted by a vehicle body dynamics module at a previous moment; and determining, by the vehicle body dynamics module, motion data of the vehicle body and a tire normal load at the current moment based on the control instruction and the tire translational force at the current moment, the motion data of the vehicle body at the current moment being used for vehicle simulation, and the tire normal load at the current moment being used to determine a tire translational force at a next moment.

Abstract: The technologies described herein relate to a computing architecture in an autonomous vehicle (AV). The AV includes a radar system that includes a local data processing device. The AV also includes a centralized data processing device. The local data processing device is configured to perform processing on raw sensor data generated by the radar system to form a feature vector. The local data processing device transmits the feature vector to the centralized data processing device, which performs further processing to identify an object in an environment of the AV.

Abstract: A self-driving vehicle implements a deep reinforcement learning based model. The self-driving vehicle comprise one or more sensors configured to capture sensor data of an environment of the self-driving vehicle, a control system configured to navigate the self-driving vehicle, and a controller to determine and provide instructions to the control system. The controller implements a deep reinforcement learning based model that inputs the sensor data captured by the sensors to determine actions to perform by the control system. The model includes an archive storing states reachable by an agent in a training environment, each state stored in the archive is associated with a trajectory for reaching the state. The archive is generated by visiting states stored in the archive and performing actions to explore and find new states. New states are stored in the archive with their trajectories.

Abstract: A vehicle driving support device performs driving support of supporting a driver's driving of a host vehicle based on traffic information. The vehicle driving support device performs a first process of acquiring traffic information associated with traveling of the host vehicle as first traffic information from forward information of the host vehicle detected by a forward information detecting device, performs a second process of acquiring traffic information associated with traveling of the host vehicle out of traffic information stored in a database as second traffic information by comparing a current position of the host vehicle with map data stored in the database, and performs the driving support based on the first traffic information even when the second traffic information is acquired until a predetermined first condition is satisfied when the first traffic information has been acquired.

Abstract: Systems, apparatuses and methods may provide for technology that conducts a real-time analysis of interior sensor data associated with a vehicle, exterior sensor data associated with the vehicle and environmental data associated with the vehicle. Additionally, the technology may determine whether a hazard condition exists based on the real-time analysis, wherein the hazard condition includes a deviation of a current behavior waveform from a reference behavior waveform by a predetermined amount. In one example, a safety measure is triggered with respect to the vehicle if the hazard condition exists. The safety measure may be selected based on a reaction time constraint associated with the hazard condition.

Abstract: There is provided a smart control system including a host, at least one sensor and an informing device. The host identifies the activity of a specific member according to the detection result of the at least one sensor, and informs the specific member to take medicine on time or avoid eating specific ingredients using the informing device.

Abstract: The present invention relates generally to providing a system and method for monitoring devices relative to a user defined geographic area using an enablement platform for building web sites and web applications using data storage, management and publication capabilities of hosted web services. The system and method for monitoring M2M devices relative to a user defined geographic area (geofence) are built on existing AerCloud concepts by allowing user to define location attributes and by using the user defined location attributes to configure and evaluate geofence parameters and issue alerts if the devices are performing outside the geofence parameters.

Abstract: A system for securing the parking of an automobile vehicle parked on a parking area (Z), the vehicle including two steering wheels, and a actuator to modify the orientation of the steering wheels without the action of the driver, the system including a control unit (UC), a system for detecting a parked state of the automobile vehicle, a sensor for detecting the slope of the parking area, sensor for determining the orientation of the steering wheels with respect to the axis of the vehicle, the control unit (UC) being configured to determine the orientation of the steering wheels to put the vehicle in a decreased danger configuration in case of an unwanted movement of the vehicle, and to sending an instruction to the actuator to orientate the steering wheels according to the orientation determined by the control unit (UC).

Abstract: User data stored in memory of a shared vehicle is managed in a secure manner. In some embodiments, user data stored in the vehicle is deleted in response to receiving a communication that initiates closing of usage of the vehicle (e.g., from a client device of a user that is operating the vehicle). A new user of a vehicle is prevented from being able to read or use private data of a prior user. In some embodiments, when authorizing usage of a vehicle by a new user, the vehicle is caused to delete the prior user data. After the prior user data has been deleted, a notification is received from the vehicle confirming that the vehicle has deleted the prior user data.

Abstract: An autonomous driving system for an autonomous vehicle includes an automated driving controller wirelessly connected to a towing taxi. The automated driving controller determines the autonomous driving system is non-functional. In response to determining the autonomous driving system is non-functional, the automated driving controller generates a notification indicating the autonomous driving system is non-functional. The automated driving controller receives, from the towing taxi, a current data string including a data point corresponding to a current point in time in combination with a predicted data point for each of one or more predicted points of time in the future. The current data string is compared with a previous data string recorded at a previous point in time. In response to determining the current data string matches the previous data string, the automated driving controller determines one or more driving maneuvers for the autonomous vehicle based on the current data string.

Abstract: A method ensures that a vehicle can safely pass a traffic light. The vehicle includes a processor and a light sensor. The method includes receiving traffic data, establishing a speed profile, establishing a control distance, the processor establishing, in accordance with the speed profile, a control distance at which braking ensures that the vehicle stops safely before the position of the traffic light, adjustment according to the speed profile, detecting the state of the traffic light when the vehicle is at the control distance from the traffic light, and the processor activating braking if the traffic light is red.

Abstract: A cooperating vehicle (110) is a transporting vehicle which includes sensors for road measurement. An in-vehicle apparatus (300) mounted on the cooperating vehicle acquires measured data obtained by the cooperating vehicle, generates point cloud data on the basis of the measured data, and transmits the point cloud data to a map server apparatus (200). The map server apparatus receives the point cloud data from the in-vehicle apparatus, and generates map data on the basis of the point cloud data.

Abstract: A method for checking at least one driving environment sensor of a vehicle is provided. The vehicle is located on a digital map, features of stored stationary objects in an environment of the vehicle, which are expected to be recognized by the driving environment sensor, are identified in the digital map and the environment of the vehicle is sensed using the driving environment sensor. A degradation of the driving environment sensor is deduced if the features to be recognized according to expectations are not recognized by the driving environment sensor or if features actually recognized by the driving environment sensor deviate strongly from the features to be recognized according to expectations.

Abstract: Systems, methods, and other embodiments described herein relate to controlling a vehicle system when the vehicle system is under control of a user. In one embodiment, a method includes predicting a start of a user sneezing episode. The method includes identifying a plurality of phases in the user sneezing episode, and controlling the vehicle system based on which one of the plurality of phases is active.

Abstract: A method for estimating a longitudinal force difference ?Fx acting on steered axle wheels of a vehicle, the method comprising obtaining data from the vehicle related to an applied steering torque Msteer associated with the steered axle wheels, obtaining a scrub radius value rs associated with the steered axle wheels, and estimating the longitudinal force difference ?Fx, based on the obtained data and on the scrub radius rs, as proportional to the applied steering torque Msteer and as inversely proportional to the scrub radius rs.

Abstract: A vehicle device may execute one or more neural networks (and/or other artificial intelligence), such as based on input from one or more of the cameras and/or other sensors associated with the dash cam, to intelligently detect safety events in real-time. The vehicle device may further pass the input to a backend server for further analysis and the backend server can detect safety events based on the input. The vehicle device may analyze the output of the vehicle device and the output of the backend server to determine whether the output of the vehicle device is correct. If the output of the vehicle device is incorrect, the vehicle device can adjust how the vehicle device identifies safety events.

Abstract: A method includes detecting, via a processor of an autonomous vehicle, an upcoming downhill road segment of a route on which the autonomous vehicle is currently travelling. The detection is based on map data, camera data, and/or inertial measurement unit (IMU) data. In response to detecting the upcoming downhill road segment, a descent plan is generated for the autonomous vehicle. The descent plan includes a speed profile and a brake usage plan. The brake usage plan specifies a non-zero amount of retarder usage and an amount of foundation brake usage for a predefined time period. The method also includes autonomously controlling the autonomous vehicle, based on the descent plan, while the autonomous vehicle descends the downhill road segment.

Abstract: Embodiments describe a vehicle configured with a brain machine interface (BMI) for a vehicle computing system to control vehicle functions using electrical impulses from motor cortex activity in a user's brain. A BMI training system trains the BMI device to interpret neural data generated by a motor cortex of a user and correlate the neural data to a vehicle control command associated with a neural gesture emulation function. A BMI system onboard the vehicle may receive a neural data feed of neural data from the user using the trained BMI device, determine, a user intention for a control instruction to control a vehicle infotainment system using the neural data feed, and perform an action based on the control instruction. The vehicle may further include a headrest configured as a Human Machine Interface (HMI) device that reads the electrical impulses without invasive electrode connectivity.