The integration of audio analytics in autonomous vehicles has been possible with the help of artificial intelligence (AI) services and machine learning (ML) services. Its huge transformative aspects crucially redefined, advanced, and made the autonomous car industry and self-driving cars successful, thanks to artificial intelligence development. It became more customer-centric to enhance the experience and satisfaction metrics.
Audio classification, natural language processing (NLP), voice/speech, and sound recognition comprised in audio analytics for various autonomous vehicles leverage revolutionary machine learning services. Artificial intelligence development offers powerful algorithms, tweaking today’s voice/speech recognition to be more accurate, reliable, and uninterrupted with cloud computing/wireless internet connectivity and massive edge processing power.
The constant advancement of artificial intelligence services through relentless research paves to ground-breaking innovation to make the best driverless/self-driving vehicles. The extensive use of new machine learning or deep learning algorithms in modern-day autonomous vehicles capably powers its audio analytics part. They are highly feature-rich and are fully utilized by feeding into autonomous vehicle systems for even more efficiency.
The various algorithms used today are:
- k-NN (K Nearest Neighbour)
- SVM (Support Vector Machine)
- EBT (Ensemble Bagged Trees)
- Deep Neural Networks (DNN)
- and Natural Language Processing (NLP).
Model Training for Audio Analytics
Noises are highly reduced from the source audio data through pre-processing methods so that the required audio features can be extracted from the same. MFCC (Mel-frequency cepstral coefficient) is one of the audio features that are used for carrying out noise reduction along with the help of statistical features like Kurtosis, and Variance as well. The frequency bands of MFCC have equally spaced frequency bands according to the Mel scale, which closely resembles the human auditory system’s response factor. As per this deduction, an audio analytics model is trained accordingly using machine learning services. The model trained with artificial intelligence services is then integrated into the autonomous vehicle’s systems having multiple microphones so that it receives a real-time audio stream from the vehicle. The received audio feed will then be pre-processed by the model for extracting the desired features so that it can properly recognize the audio, to make accurate inferences enabling the right decision-making in autonomous vehicles.
The other ground-breaking technology augmenting autonomous vehicle effectiveness and end-user trust factor is NLP technology. With NLP, users or passengers can control the autonomous vehicle through voice commands enabling a highly rich, and interactive experience. It can be used to make stops at specific locations, change routes, switch on/off the vehicle lights, open and close vehicle doors, etc.
Audio Analytics Use Cases for Autonomous Vehicles
Emergency Siren Detection
It detects and recognizes the siren sound of approaching emergency vehicles such as ambulances, fire trucks, or police vehicles. This detection system or mechanism trained by deep learning models as well as machine learning services models can be used to perform classification and regression analysis. Through artificial intelligence development, the model is trained with colossal amounts of data for it to accurately identify the emergency siren sound and non-emergency sounds. Upon identifying siren sounds, it enables the autonomous vehicle to make the right decision by avoiding any dangerous scenarios by pulling the autonomous vehicle over and giving the emergency vehicle way to pass by.
Engine Sound Anomaly Detection
It can automatically detect possible engine failures at a very early stage by analyzing the vehicle’s engine sound. The vehicle will have a specific sound when it has no engine problems or when it’s working under normal conditions, and a completely different one when there are any engine problems/faults. Machine learning services are mainly responsible for this constant engine monitoring so that it detects any anomalies by analyzing the engine sounds. This use case model will help ensure the autonomous vehicle’s engine health is top-notch with prompt alerts delivered early so that it helps users to make the right decisions avoiding dangerous scenarios, complete engine failure, or break-downs.
Lane Change on Honking
A model that helps an autonomous vehicle to change lanes when another vehicle from behind honks in order to pass by. The training of the model is done with the right set of accurate artificial intelligence development data to enable the autonomous vehicle to make lane-changing decisions almost similar to humans when hearing a honking vehicle from behind.
In-Car Interaction
This is done by integrating NLP along with the artificial intelligence services algorithms so that there is accurate processing of human speech interaction with the autonomous vehicle. Using voice commands, the user can easily control the vehicle or speak with it for better interaction that can help make better decisions. Assigning the self-driving vehicle to help reach the user at a specific location and time, giving the vehicle a name, or making it perform even more complex tasks such as having to make multiple stops, and many more can be done with this model use case. Although to have it pack more of a human-like behavior or effective presence of mind and better interpretational capabilities, it requires even more powerful NLPs that are needed to help it make perfectly logical decisions. It avoids any dangerous scenarios by eliminating any blind obeying of user commands by the autonomous vehicle which would otherwise have serious consequences.
So these are the vital aspects of machine learning-based audio analytics technology used in autonomous cars. The underlying artificial intelligence development algorithms and other things are what make the autonomous vehicle even more safe and reliable to humans. This will ultimately enhance the passenger experience, on-road safety, along with prompt and timely engine maintenance of autonomous vehicles.