Baidu USA LLC

Wesley Reynolds

Qi Luo

Kecheng Xu

Hongyi Sun

Zejun Lin

Jiangtao Hu

Jinghao Miao

Wei Wei Wang

Wei Wang

Feng Quan Liu

Yu Long Cao

Weiman Lin

Youling Zou

Feng Liu

In one embodiment, an emergency vehicle detection system can be provided in the ADV travelling on a road to detect the presence of an emergency vehicle in a surrounding environment of the ADV using both audio data and visual data. The emergency vehicle detection system can use a trained neutral network to independently generate a detection result from the audio data, and use another trained network to independently generate another detection result from the visual data. The emergency vehicle detection system can fuse the two detection results to determine the position and moving direction of the emergency vehicle. The ADV can take appropriate actions in response to the position and moving direction of the emergency vehicle.

Emergency vehicle audio and visual detection post fusion

Sound signals are received by one or more microphones disposed at an ADV. The sound signals are analyzed to extract a feature of a road on which the ADV is driving. A road condition of the road is determined based on the extracted feature. A path planning and speed planning is performed to generate a trajectory based on the road condition. The ADV is controlled to drive autonomously according to the generated trajectory.

Audio-based technique to sense and detect the road condition for autonomous driving vehicles

Sound signals that are not audible are generated by one or more speakers disposed in the vehicle. Reflected sound signals from a driver or a passenger of the vehicle that are not audible are received by one or more microphones disposed in the vehicle. A behavior-induced acoustic pattern is detected based on the reflected ultrasound signals. The behavior-induced acoustic pattern is analyzed to recognize a behavior of the driver or the passenger of the vehicle. A response or an alert is generated according to the recognized behavior of the driver or the passenger in the vehicle.

In-vehicle acoustic monitoring system for driver and passenger

Systems and methods for generating labelled audio data and onboard validation of the labelled audio data utilizing an autonomous driving vehicle (ADV) while the ADV is operating within a driving environment are disclosed. The method includes recording a sound emitted by an object within the driving environment of the ADV, and converting the recorded sound into audio samples. The method further includes labelling the audio samples, and refining the labelled audio samples to produce refined labelled audio data. The refined labelled audio data is utilized to subsequently train a machine learning algorithm to recognize a sound source during autonomous driving of the ADV. The method further includes generating a performance profile of the refined labelled audio data based at least on the audio samples, a position of the object, and a relative direction of the object. The position of the object and the relative direction of the object are determined by a perception system of the ADV.

Audio logging for model training and onboard validation utilizing autonomous driving vehicle

According to various embodiments, systems, methods, and mediums for operating an autonomous driving vehicles (ADV) are described. The embodiments use a number of machine learning models to extract features individually from audio data and visual data captured by sensors mounted on the ADV, and then to fuse these extracted features to create a concatenated feature vectors. The concatenated feature vector is provided to a multiplayer perceptron (MLP) as input to generate a detection result related to the presence of an emergency vehicle in the surrounding environment. The detection result can be used by the ADV to take appropriate actions to comply with the local traffic rules.

Machine learning model to fuse emergency vehicle audio and visual detection

Systems and methods for sound source detection and localization utilizing an autonomous driving vehicle (ADV) are disclosed. The method includes receiving audio data from a number of audio sensors mounted on the ADV. The audio data comprises sounds captured by the audio sensors and emitted by one or more sound sources. Based on the received audio data, the method further includes determining a number of sound source information. Each sound source information comprises a confidence score associated with an existence of a specific sound. The method further includes generating a data representation to report whether there exists the specific sound within the driving environment of the ADV. The data representation comprises the determined sound source information. The received audio data and the generated data representation are utilized to subsequently train a machine learning algorithm to recognize the specific sound source during autonomous driving of the ADV in real-time.

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Sunnyvale, CA, United States of America

Wesley Reynolds