Abstract: A model of a system of an intelligent vehicle is trained and optimized using system operation data of the intelligent vehicle in a normal running state. The system operation data of the intelligent vehicle in a running state is collected in real time. Sensor data of the system operation data is de-noised, and feature extraction and screening are performed for a fatal sensor fault to reconstruct the system operation data. The reconstructed system operation data is inputted into the trained model to output system state data of the intelligent vehicle in the running state. The system state data is compared with a set threshold. If the system state data exceeds the set threshold, an actuator corresponding to the system state data is determined to have a fault. In addition, a system for a fault diagnosis of the intelligent vehicle is further provided.

Abstract: Provided are a system and a method for testing an ability of an automated vehicle to pass a traffic circle without traffic lights. The system includes an automated vehicle, a traffic circle, a control center, an attitude sensor and a panorama camera. In the method, the control center receives a test request and then sends a driving command to drive the automated vehicle to enter the traffic circle to start the test. The attitude sensor obtains a tilt angle and sends it to the control center in real time. The panorama camera is configured to obtain a driving trajectory, a driving speed and a state of turn signals of the automated vehicle. The obtained information is compared to a standard by the control center to evaluate the ability of the automated vehicle to pass the traffic circle without traffic lights.

Abstract: A model of a system of an intelligent vehicle is trained and optimized using system operation data of the intelligent vehicle in a normal running state. The system operation data of the intelligent vehicle in a running state is collected in real time. Sensor data of the system operation data is de-noised, and feature extraction and screening are performed for a fatal sensor fault to reconstruct the system operation data. The reconstructed system operation data is inputted into the trained model to output system state data of the intelligent vehicle in the running state. The system state data is compared with a set threshold. If the system state data exceeds the set threshold, an actuator corresponding to the system state data is determined to have a fault. In addition, a system for a fault diagnosis of the intelligent vehicle is further provided.

Abstract: Provided are a method and a device for constructing autonomous driving test scenes, a terminal and a readable storage media. Primitive scenes are extracted from real traffic scenes to establish a primitive scene description model. The values of the description variables are selected from distribution intervals of the description variables of the primitive scenes, and the description variables are randomly sampled based on the distribution of the description variables to generate test primitive scenes by adopting an importance sampling based Monte Carlo method. The test primitive scenes are recombined according to the parameters of the elements of the test scene to generate the test scene. The present invention can directly simulate the actual complex system, so as to avoid the result distortion caused by the simplification of the complex system.

Abstract: Provided are a method and a device for constructing autonomous driving test scenes, a terminal and a readable storage media. Primitive scenes are extracted from real traffic scenes to establish a primitive scene description model. The values of the description variables are selected from distribution intervals of the description variables of the primitive scenes, and the description variables are randomly sampled based on the distribution of the description variables to generate test primitive scenes by adopting an importance sampling based Monte Carlo method. The test primitive scenes are recombined according to the parameters of the elements of the test scene to generate the test scene. The present invention can directly simulate the actual complex system, so as to avoid the result distortion caused by the simplification of the complex system.

Abstract: The present invention discloses a target cross-domain detection and understanding method, system and equipment and a storage medium.

Abstract: Disclosed is an LTE-V based Internet of Vehicles communication test system and test method. The test system includes an ENodeB base station, a roadside test unit, user test terminal, LTE-V core network and local server group. Mobile communication technology is applied to the field of Internet of Vehicles communication, and two technical schemes are used, i.e., wide-area centralized cellular communication and short-range distributed direct communication corresponding to the network architectures based on access network-user terminal and ProSe direct communication interface, respectively. Not only the communication transmission support with large broadband in wide coverage can be supported, but also to achieve low latency and highly reliable communication services between vehicle and vehicle, vehicle and base station, base station and base station, to meet the needs of road safety and traffic efficiency applications.

Abstract: Provided are a system and a method for testing an ability of an automated vehicle to pass a traffic circle without traffic lights. The system includes an automated vehicle, a traffic circle, a control center, an attitude sensor and a panorama camera. In the method, the control center receives a test request and then sends a driving command to drive the automated vehicle to enter the traffic circle to start the test. The attitude sensor obtains a tilt angle and sends it to the control center in real time. The panorama camera is configured to obtain a driving trajectory, a driving speed and a state of turn signals of the automated vehicle. The obtained information is compared to a standard by the control center to evaluate the ability of the automated vehicle to pass the traffic circle without traffic lights.

Abstract: Provided herein relates to the performance testing of automated vehicles, and more particularly to a system and a method for testing cooperative driving capability of an automated vehicle. The system includes a target vehicle, a test road and a control center, where the automated vehicle and the target vehicle are located on the test road, and the control center is located beside the test road. A speed sensor and a binocular camera are provided on the automated vehicle. The speed sensor and the binocular camera are connected to the control center through a wireless communication device, respectively. An on-board device is provided on the target vehicle and is connected to the control center through the wireless communication device. The method provided herein is used to determine the responsiveness of automated vehicles according to the speed relationship and distance between the automated vehicle and the target vehicle.

Abstract: An IEEE 802.11p protocol-based vehicle-to-roadside and vehicle-to-vehicle communication test method oriented to the Internet of Vehicles. The method includes: driving a host vehicle and a target vehicle at a same speed on a road segment; the host vehicle being located behind the target vehicle; a constant distance is maintained between the host vehicle and the target vehicle; the host vehicle is equipped with a host vehicular communication unit, the target vehicle is equipped with a target vehicular communication unit communicating with the host vehicular communication unit; calculating a throughput and a round trip time RTT from the target vehicular communication unit to the host vehicular communication unit; repeating driving the host vehicle and target vehicle N times, and calculating an average throughput and an average round trip time (RTT) of the N times; and calculating a network performance parameter ? according to the average throughput and the RTT.

Abstract: A system for perceiving and co-processing intelligent connected vehicle-oriented scene image data, including a device for perceiving and processing single-mobile intelligent agent image data, a roadside infrastructure apparatus and a remote server. An in-vehicle image processing platform in the device enables single-view image data processing by performing algorithms such as target detection and recognition, and scene stream estimation. A roadside image processing platform in the roadside infrastructure apparatus enables depression angle image data processing and multi-view image fusion processing by performing algorithms such as target detection and recognition, and image fusion.

Abstract: Disclosed is an LTE-V based Internet of Vehicles communication test system and test method. The test system includes an ENodeB base station, a roadside test unit, user test terminal, LTE-V core network and local server group. Mobile communication technology is applied to the field of Internet of Vehicles communication, and two technical schemes are used, i.e., wide-area centralized cellular communication and short-range distributed direct communication corresponding to the network architectures based on access network-user terminal and ProSe direct communication interface, respectively. Not only the communication transmission support with large broadband in wide coverage can be supported, but also to achieve low latency and highly reliable communication services between vehicle and vehicle, vehicle and base station, base station and base station, to meet the needs of road safety and traffic efficiency applications.

Abstract: An IEEE 802.11p protocol-based vehicle-to-roadside and vehicle-to-vehicle communication test method oriented to the Internet of Vehicles. The method includes: driving a host vehicle and a target vehicle at a same speed on a road segment; the host vehicle being located behind the target vehicle; a constant distance is maintained between the host vehicle and the target vehicle; the host vehicle is equipped with a host vehicular communication unit, the target vehicle is equipped with a target vehicular communication unit communicating with the host vehicular communication unit; calculating a throughput and a round trip time RTT from the target vehicular communication unit to the host vehicular communication unit; repeating driving the host vehicle and target vehicle N times, and calculating an average throughput and an average round trip time (RTT) of the N times; and calculating a network performance parameter ? according to the average throughput and the RTT.