Sense of hearing is crucial for autonomous vehicles (AVs) to better perceive its surrounding environment. Although visual sensors of an AV, such as camera, lidar, and radar, help to see its surrounding environment, an AV cannot see beyond those sensors line of sight. On the other hand, an AV s sense of hearing cannot be obstructed by line of sight. For example, an AV can identify an emergency vehicle s siren through audio classification even though the emergency vehicle is not within the line of sight of the AV. Thus, auditory perception is complementary to the camera, lidar, and radar-based perception systems. This paper presents a deep learning-based robust audio classification framework aiming to achieve improved environmental perception for AVs. The presented framework leverages a deep Convolution Neural Network (CNN) to classify different audio classes. UrbanSound8k, an urban environment dataset, is used to train and test the developed framework. Seven audio classes i.e., air conditioner, car horn, children playing, dog bark, engine idling, gunshot, and siren, are identified from the UrbanSound8k dataset because of their relevancy related to AVs. Our framework can classify different audio classes with 97.82% accuracy. Moreover, the audio classification accuracies with all ten classes are presented, which proves that our framework performed better in the case of AV-related sounds compared to the existing audio classification frameworks.

Do you know what will happen if you honk your car horn at an AI self-driving car? It used to be that honking a car horn was quite customary and an expected element in the act of driving. Indeed, when I first learned to drive, the driver training class included a brief segment devoted to the use of the car horn (yes, that used to be a normal part of learning to drive). We were mindfully instructed on the use of a car horn. For example, we began by employing the horn in a delicate fashion such as lightly tapping the horn to generate some casual and modestly alerting toots. We also were later taught to lean on the horn and generate an ear-shattering blast, just in case an outstretched use of the car horn was warranted. All told, the notion was that you needed to know how to use your car horn for a wide variety of circumstances. The horn was integral to driving a car. Akin to knowing how to steer, speed up, slow down, and the rest, you likewise should be versed in the use of the car horn.

The'why' of the DepthAI (that satisfyingly rhymes) is we're actually shooting for a final product which we hope will save the lives of people who ride bikes, and help to make bike commuting possible again for many. What we envisioned is a technology-equivalent of a person riding backwards on your bike holding a fog horn and an ambulance-LED strip, who would tap you on the shoulder when they noticed a distracted driver, and would use the LED strip and the horn judiciously to get the attention of distracted drivers - to get them to swerve out of the way. In working towards solving this problem, we discovered there was no solution on the market for the real-time situational awareness needed to accomplish this. So we decided to make it. In doing that, we realized how useful such an embeddable device would be across so many industries, and decided to build it as a platform not only for ourselves, but also for anyone else who could benefit from this real-time object localization (what objects are, and where they are in the physical world). It's the platform we will use to develop Commute Guardian (and other applications), and we hope it will be equally useful to you in your prototypes and products.

We all got exposed to different sounds every day. How about teaching computer to classify such sounds automatically into categories! In this blog post, we will learn techniques to classify urban sounds into categories using machine learning. Earlier blog posts covered classification problems where data can be easily expressed in vector form. For example, in the textual dataset, each word in the corpus becomes feature and tf-idf score becomes its value.

We all got exposed to different sounds every day. How about teaching computer to classify such sounds automatically into categories! In this blog post, we will learn techniques to classify urban sounds into categories using machine learning. Earlier blog posts covered classification problems where data can be easily expressed in vector form. For example, in the textual dataset, each word in the corpus becomes feature and tf-idf score becomes its value.

Prototype vehicles are now able to recognize when honks are appropriate and even modulate how they use the horn, the company said in its most recent monthly report. "As our honking algorithms improved, we've begun broadcasting our car horn to the world," the report said. "If another vehicle is slowly reversing towards us, we might sound two short, quieter pips as a friendly heads up to let the driver know we're behind," it continued. "However, if there's a situation that requires more urgency, we'll use one loud sustained honk." Driverless cars may be the future, but we're not getting there all at once.