Abstract: A metal-air battery and methods for generating electricity in a metal-air battery are described herein. The battery and the method includes heating an anhydrous salt to obtain a molten salt electrolyte; contacting the molten salt electrolyte to at least one cathode communicating with air; reducing air at the cathode to obtain oxygen ions for diffusing through the molten salt electrolyte; oxidizing at least one metal anode by the oxygen ions in the electrolyte thereby generating electricity and forming a metal anode oxide; and cooling at least one section of the metal-air battery for precipitating the metal anode oxide.

Abstract: In one embodiment, a process is performed during controlling Autonomous Driving Vehicle (ADV). A confidence level associated with a sensed obstacle is determined. If the confidence level is below a confidence threshold, and a distance between the ADV and a potential point of contact with the sensed obstacle is below a distance threshold, then performance of a driving decision is delayed. Otherwise, the driving decision is performed to reduce risk of contact with the sensed obstacle.

Abstract: The present disclosure discloses a contact angle measuring device, including a light source, a container, a photodetector, a bubble generating unit, and a processing unit. The container includes a first and a second side walls that are opposite. The first side wall is made of a light-transmitting material, the container is filled with a liquid with light transmission inside. The monochromatic light emitted by the light source passes through the first side wall and enters an interface between the first side wall and the liquid. The bubble generating unit is configured for generating a bubble that is in contact with an inner surface of the first side wall. The photodetector is configured for detecting light intensity distribution of the monochromatic light through the liquid.

Abstract: 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.

Abstract: An obstacle state evolution of a spatial position of a moving obstacle over a period of time is determined. A lane-obstacle relation evolution of the moving obstacle with each of one or more lanes near the moving obstacle over the period of time is further determined. An intended movement of the moving obstacle is predicted based on the obstacle state evolution and the lane-obstacle evolution. Thereafter, a trajectory of the ADV is planned to control the ADV to avoid a collision with the moving obstacle based on the predicted intended movement of the moving obstacle. The above process is iteratively performed for each of the moving obstacles detected within a predetermined proximity of the ADV.

Abstract: 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.

Abstract: In one embodiment, an autonomous driving system of an ADV perceives a driving environment surrounding the ADV based on sensor data obtained from various sensors, including detecting one or more lanes and at least a moving obstacle or moving object. For each of the lanes identified, an NN lane feature encoder is applied to the lane information of the lane to extract a set of lane features. For a given moving obstacle, an NN obstacle feature encoder is applied to the obstacle information of the obstacle to extract a set of obstacle features. Thereafter, a lane selection predictive model is applied to the lane features of each lane and the obstacle features of the moving obstacle to predict which of the lanes the moving obstacle intends to select.

Abstract: 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.

Abstract: In one embodiment, an exemplary method includes receiving, at a simulation platform, a record file recorded by a manually-driving ADV on a road segment, the simulation platform including a first encoder, a second encoder, and a performance evaluator; simulating automatic driving operations of a dynamic model of the ADV on the road segment based on the record file, the dynamic model including an autonomous driving module to be evaluated. The method further includes: for each trajectory generated by the autonomous driving module during the simulation: extracting a corresponding trajectory associated with the manually-driving ADV from the record file, encoding the trajectory into a first semantic map and the corresponding trajectory into a second semantic map, and generating a similarity score based on the first semantic map and the second semantic map. The method also includes generating an overall performance score based on each similarity score.

Abstract: One or more outputs from a planning module of an ADV are received. Data of a driving environment of the ADV is received. A performance of the planning module is evaluated by determining a score of the performance of the planning module based on the data of the driving environment and the one or more outputs from the planning module. Whether the one or more outputs from the planning module violates at least one of a set of safety rules is determined. The score is determined being larger than a predetermined threshold in response to determining that the one or more outputs from the planning module violate at least one of the set of safety rules. Otherwise, the score is determined based on a machine learning model. The planning module is modified by tuning a set of parameters of the planning module based on the score.

Abstract: According to various embodiments, systems, methods, and media for evaluating an open space planner in an autonomous vehicle are disclosed. In one embodiment, an exemplary method includes receiving, at a profiling application, a record file recorded by the ADV while driving in an open space using the open space planner, and a configuration file specifying parameters of the ADV; extracting planning messages and prediction messages from the record file, each extracted message being associated with the open space planner. The method further includes generating features from the planning message and the prediction messages in view of the specified parameters of the ADV; and calculating statistical metrics from the features. The statistical metrics are then provided to an automatic tuning framework for tuning the open space planner.

Abstract: According to various embodiments, systems and methods described in the disclosure combine mapped features with point cloud features to improve object detection precision of an autonomous driving vehicle (ADV). The map features and the point cloud features can be extracted from a perception area of the ADV within a particular angle view at each driving cycle based on a position of the ADV. The map features and the point cloud features can be concatenated and provided to a neutral network for object detections.

Abstract: A metal-air battery and methods for generating electricity in a metal-air battery are described herein. The battery and the method includes heating an anhydrous salt to obtain a molten salt electrolyte; contacting the molten salt electrolyte to at least one cathode communicating with air; reducing air at the cathode to obtain oxygen ions for diffusing through the molten salt electrolyte; oxidizing at least one metal anode by the oxygen ions in the electrolyte thereby generating electricity and forming a metal anode oxide; and cooling at least one section of the metal-air battery for precipitating the metal anode oxide.

Abstract: 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.

Abstract: 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.

Abstract: 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.

Abstract: 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.

Abstract: Systems and methods for automatic generation of labelled audio data are disclosed. The method is performed by an autonomous driving system (ADS) of an autonomous driving vehicle (ADV). The method includes recording a sound emitted by an object within a driving environment, and converting the recorded sound into audio data. The method further includes capturing at least one position of the object while the sound is being recorded. The method further includes automatically labelling the audio data using the captured at least one position of the object as an audio label, to generate labelled audio data, where the labelled audio data is utilized to subsequently train a machine learning algorithm to recognize a sound source during autonomous driving of the ADV.

Abstract: 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.

Abstract: 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.