Abstract: Embodiments of the present disclosure provide a method and an apparatus for generating a simulation scene. The method includes: acquiring scene parameters of a benchmark scene, where a dimensionality of the scene parameters of the benchmark scene is M; inputting the scene parameters of the benchmark scene into an encoder that is trained, and acquiring encoding parameters according to an output result of the encoder, where a dimensionality of the encoding parameters is N, and N<M; adjusting the encoding parameters to obtain adjusted encoding parameters, and inputting respectively the adjusted encoding parameters into a decoder that is trained; and generating a simulation scene according to the scene parameters of the reconstructed scene that are output by the decoder. According to the method, generating massive simulation scenes similar to the benchmark scene based on the benchmark scene can be achieved, which meets the diversity requirements for the simulation scenes.

Abstract: Embodiments of the present disclosure provide a method and an apparatus for generating a simulation scene. The method includes: acquiring scene parameters of a benchmark scene, where a dimensionality of the scene parameters of the benchmark scene is M; inputting the scene parameters of the benchmark scene into an encoder that is trained, and acquiring encoding parameters according to an output result of the encoder, where a dimensionality of the encoding parameters is N, and N<M; adjusting the encoding parameters to obtain adjusted encoding parameters, and inputting respectively the adjusted encoding parameters into a decoder that is trained; and generating a simulation scene according to the scene parameters of the reconstructed scene that are output by the decoder. According to the method, generating massive simulation scenes similar to the benchmark scene based on the benchmark scene can be achieved, which meets the diversity requirements for the simulation scenes.

Abstract: Provided are a method and an apparatus for acquiring a control amount for a performance test of an unmanned vehicle, including: acquiring coordinates of y key points on a trajectory of a simulated obstacle vehicle and speeds at the y key points, where y?2; acquiring trajectory equations of the obstacle vehicle between every two adjacent key points according to the coordinates of the y key points, where the trajectory equation is an nth degree polynomial equation, n?2; acquiring accelerations of the obstacle vehicle between every two adjacent key points according to speeds at the y key points and a trajectory curve corresponding to the trajectory equation; for each running duration, according to the accelerations, acquiring a dynamic attribute and a coordinate when the obstacle vehicle has run for the running duration according to trajectories corresponding to the trajectory equations.

Abstract: Embodiments of the present disclosure provide a method and an apparatus for generating a simulation scene. The method includes: acquiring scene parameters of a benchmark scene, where a dimensionality of the scene parameters of the benchmark scene is M; inputting the scene parameters of the benchmark scene into an encoder that is trained, and acquiring encoding parameters according to an output result of the encoder, where a dimensionality of the encoding parameters is N, and N<M; adjusting the encoding parameters to obtain adjusted encoding parameters, and inputting respectively the adjusted encoding parameters into a decoder that is trained; and generating a simulation scene according to the scene parameters of the reconstructed scene that are output by the decoder. According to the method, generating massive simulation scenes similar to the benchmark scene based on the benchmark scene can be achieved, which meets the diversity requirements for the simulation scenes.

Abstract: The present disclosure provides a method and apparatus for simulation test of autonomous driving of a vehicle, an apparatus and a computer-readable storage medium. In embodiments of the present disclosure, a simulation-activating command is sent to the at least two simulation nodes so that each simulation node in the at least two simulation nodes simulates the designated traffic environment and runs the corresponding vehicle decision planning algorithm, then the vehicle state data and the vehicle decision route of said each simulation node are obtained so that the vehicle state data and the vehicle decision route of said each simulation node can be output.

Abstract: Embodiments of the present disclosure provide a method and an apparatus for generating a simulation scene. The method includes: acquiring scene parameters of a benchmark scene, where a dimensionality of the scene parameters of the benchmark scene is M; inputting the scene parameters of the benchmark scene into an encoder that is trained, and acquiring encoding parameters according to an output result of the encoder, where a dimensionality of the encoding parameters is N, and N<M; adjusting the encoding parameters to obtain adjusted encoding parameters, and inputting respectively the adjusted encoding parameters into a decoder that is trained; and generating a simulation scene according to the scene parameters of the reconstructed scene that are output by the decoder. According to the method, generating massive simulation scenes similar to the benchmark scene based on the benchmark scene can be achieved, which meets the diversity requirements for the simulation scenes.

Abstract: Provided are a method and an apparatus for acquiring a control amount for a performance test of an unmanned vehicle, including: acquiring coordinates of m key points on a trajectory of a simulated obstacle vehicle and speeds at the m key points, where m?2; acquiring trajectory equations of the obstacle vehicle between every two adjacent key points according to the coordinates of the m key points, where the trajectory equation is an nth degree polynomial equation, n?2; acquiring accelerations of the obstacle vehicle between every two adjacent key points according to speeds at the m key points and a trajectory curve corresponding to the trajectory equation; for each running duration, according to the accelerations, acquiring a dynamic attribute and a coordinate when the obstacle vehicle has run for the running duration according to trajectories corresponding to the trajectory equations.

Abstract: The present disclosure provides a method and apparatus for simulation test of autonomous driving of a vehicle, an apparatus and a computer-readable storage medium. In embodiments of the present disclosure, a simulation-activating command is sent to the at least two simulation nodes so that each simulation node in the at least two simulation nodes simulates the designated traffic environment and runs the corresponding vehicle decision planning algorithm, then the vehicle state data and the vehicle decision route of said each simulation node are obtained so that the vehicle state data and the vehicle decision route of said each simulation node can be output.