Abstract: This application discloses example intelligent driving methods and vehicles to which the example methods are applied. In one example method, a decision solution set of an ego vehicle may be obtained based on perception information, and a first decision solution used for a first interaction phase between the ego vehicle and a game target is obtained from the decision solution set. While controlling the ego vehicle to travel based on the first decision solution, a determination is made that the ego vehicle and the game target meet a condition for entering a second interaction phase. A second decision solution used for the second interaction phase between the ego vehicle and the game target is obtained from the decision solution set, and the ego vehicle is controlled to travel based on the second decision solution.

Abstract: A method and an apparatus for determining a vehicle speed computing a probability distribution of action intentions based on observation information of a surrounding object. Then, a probability redistribution of the different action intentions is computed based on travel times for the vehicle to travel from a current position to risk areas corresponding to the different action intentions, motion status variations of the surrounding object with the different action intentions are predicted based on the travel times for the vehicle to travel to the risk areas corresponding to the different action intentions. Finally, the travelling speed of the vehicle is determined based on the probability redistribution of the different action intentions, the motion status variations of the surrounding object with the different action intentions, and motion status variations of the vehicle under different travelling speed control actions.

Abstract: A driving intention determining method includes obtaining status information of traffic targets in a driving environment, where the traffic targets include a traffic target in a moving state and traffic targets in a static state, and status information of the traffic targets in a static state includes at least indication information of a traffic sign and road boundary information, determining a risk field of the driving environment based on the status information of the traffic targets in the driving environment, for any other vehicle in the driving environment, determining a driving track of the other vehicle in the driving environment based on the risk field, and determining driving intention of the other vehicle based on the driving track of the other vehicle in the driving environment.

Abstract: A method and an apparatus for determining a vehicle speed computing a probability distribution of action intentions based on observation information of a surrounding object. Then, a probability redistribution of the different action intentions is computed based on travel times for the vehicle to travel from a current position to risk areas corresponding to the different action intentions, motion status variations of the surrounding object with the different action intentions are predicted based on the travel times for the vehicle to travel to the risk areas corresponding to the different action intentions. Finally, the travelling speed of the vehicle is determined based on the probability redistribution of the different action intentions, the motion status variations of the surrounding object with the different action intentions, and motion status variations of the vehicle under different travelling speed control actions.

Abstract: A driving intention determining method includes obtaining status information of traffic targets in a driving environment, where the traffic targets include a traffic target in a moving state and traffic targets in a static state, and status information of the traffic targets in a static state includes at least indication information of a traffic sign and road boundary information, determining a risk field of the driving environment based on the status information of the traffic targets in the driving environment, for any other vehicle in the driving environment, determining a driving track of the other vehicle in the driving environment based on the risk field, and determining driving intention of the other vehicle based on the driving track of the other vehicle in the driving environment.