A lane-borrowing vehicle driving method includes generating, by a control center, a first lane-borrowing driving policy of the vehicle based on a lane-borrowing requirement, a moving trend of the vehicle, and a preset traffic rule, where the lane-borrowing requirement includes a lane-borrowing driving reason, and the first lane-borrowing driving policy includes an instruction for controlling lane-borrowing driving of the vehicle, and sending, by the control center, the first lane-borrowing driving policy to the vehicle. The embodiments of this application are used for temporary lane-borrowing driving and lane-borrowing driving on a tidal lane.

Techniques for vehicle deceleration planning are discussed. The techniques include determining a first location and a first velocity of a vehicle. The techniques further include determining a second location and a second velocity of an object. Based on the first location, the second location, the first velocity, and the second velocity, a relative stopping distance between the vehicle and the object can be determined. If the relative stopping distance is less than a threshold distance, the first maximum deceleration value can be increased to a second maximum deceleration value, and the techniques determine a trajectory for the vehicle based at least in part on the second maximum deceleration value.

A vehicle includes a chassis, a driveline coupled to the chassis, and a control system. The control system is configured to monitor a condition of at least one of the vehicle, an area around the vehicle, or an operator of the vehicle; and control operation of the driveline based on the condition. Controlling the operation of the driveline includes at least one of limiting a speed at which the driveline drives the vehicle or shutting down the driveline and isolating a component of the driveline.

In one example, a method performed by a processing system including at least one processor includes receiving a plurality of data from an autonomous driving vehicle, presenting the plurality of data in a contextually aware dynamic visual indicator, generating an output that the autonomous driving vehicle is operating improperly based on an analysis of the plurality of data, and transmitting a control signal to the autonomous driving vehicle in response to the output that the autonomous driving vehicle is operating improperly to modify an operation of the autonomous driving vehicle.

The present teaching relates to method, system, and medium, for operating a vehicle. The method includes the steps of receiving Real-time data related to the vehicle are received. A current mode of operation of the vehicle is determined. A first risk associated with the current mode of operation of the vehicle is evaluated based on the real-time data in accordance with a risk model. If the first risk satisfies a first criterion, a second risk associated with switching the current mode to a different mode of operation of the vehicle is determined. The vehicle is switched from the current mode to the different mode if the second risk satisfies a second criterion.

Vehicle manipulation is performed using crowdsourced data. A camera within a vehicle is used to collect cognitive state data, including facial data, on a plurality of occupants in a plurality of vehicles. A first computing device is used to learn a plurality of cognitive state profiles for the plurality of occupants, based on the cognitive state data. The cognitive state profiles include information on an absolute time or a trip duration time. Voice data is collected and is used to augment the cognitive state data. A second computing device is used to capture further cognitive state data on an individual occupant in an individual vehicle. A third computing device is used to compare the further cognitive state data with the cognitive state profiles that were learned. The individual vehicle is manipulated based on the comparing of the further cognitive state data.

A vehicle control device includes a recognizer that is configured to recognize a peripheral situation of a vehicle and a driving controller that is configured to execute automated driving for controlling a speed and steering of the vehicle on the basis of a recognition result of the recognizer. The recognizer is configured to recognize a real-time environment including a situation of a nearby vehicle or a situation of a nearby road on which the vehicle travels, and the driving controller is configured to shunt the vehicle to a shunt location to suspend the automated driving in a case where the real-time environment corresponds to a predetermined condition, and resume the automated driving in a case where the predetermined condition is resolved after the shunt.

Simulated degraded sensor data may be generated for use in training a model. For instance, first sensor data collected by a sensor of a perception system of an autonomous vehicle may be received and converted into the simulated degraded sensor data for a particular degrading condition, such as a weather-related degrading condition. Then, the simulated degraded sensor data may be used to train a model for evaluating performance of the perception system to detect objects external to the autonomous vehicle under one or more conditions.

A vehicle is located on a digital map, with objects being stored in the digital map in a georeferenced manner. From a set of these objects stored in a georeferenced manner in the digital map, which are currently being detected by the environment sensor, the object most distant from the environment sensor is identified and a current sensor range of the environment sensor is determined on the basis of the distance of the environment sensor from the object.

A hybrid vehicle comprises an engine, an energy storage device, and an aftertreatment system comprising a SCR catalyst configured to treat constituents of an exhaust gas. A controller is operatively coupled to the engine, the energy storage device, and the after treatment system, and configured to estimate an exhaust gas temperature and flow rate of the exhaust gas based on a set of engine operating parameters. The controller determines an exhaust gas cooling rate based on the exhaust gas temperature, flow rate, and a SCR catalyst temperature, and an ambient cooling rate based on an ambient temperature, a vehicle speed and the catalyst temperature. The controller determines a SCR catalyst temperature change rate based on the exhaust gas and ambient cooling rates, and adjusts a load distribution between the engine and the energy storage device based on the SCR catalyst temperature change rate.