This article discusses various stages of autonomous driving and explores Computer Vision aspects of it in detail. Semantic segmentation is the partition of an image into coherent parts. Instance segmentation is Semantic Segmentation with the addition of identification of each unique entity in the image.
The most challenging task in Computer Vision, which is very relevant for self-driving technology, is the localization and classification of various objects in a scene (e.g. car, bus, truck, person, traffic lights, signs). It is a challenge to classify and track unique entities (e.g. person 1, person 2, car 1, car 2). For various stages in autonomous driving pipeline, such information is very valuable. Autonomous driving systems need to understand what various entities in a scene are (Instance Segmentation) and where they are in order to find best possible path.
As shown in Fig 2, autonomous driving consists of following stages in a pipeline.
It is necessary to develop rich understanding of the environment. In autonomous driving, this is called Perception.
Perception is followed by Localization, Path Planning, and Control stages.
Localization involves the task of determining positions within an environment. Using high definition maps, the software localizes the precise location of the vehicle using Simultaneous Localization And Mapping (SLAM) algorithms.
3) Path Planning
Path Planning figures out best path in a dynamically changing environment. A method is to find a path that maximizes distances from surrounding objects. Various Multi-Model (MM) algorithms are used (e.g. autonomous multiple-model, generalized pseudo-Bayesian algorithms).
Control is the final stage which generates mechanical maneuvers to update car controls (braking, acceleration, steering, etc.). Control executes actions as found by the Path Planner.
Deep means many stacked layers of CNNs. It is shown empirically that the deeper the network, the more learning capacity it has for understanding complicated scenes, such as urban driving.
One of most difficult tasks in Computer Vision is Instance Segmentation which is relevant to Autonomous Driving.
There are several schemes to perform Instance Segmentation and this is an active area of research. The most popular methods in use are:
As shown in Fig 3, from the Mask RCNN paper, describes an example architecture where segmentation masks are generated for every possible instance (Region of Interest or 'RoI'). A segmentation mask for an instance is basically a binary mask with 1s at pixel locations corresponding to an instance and 0s otherwise.
The most of the popular algorithms (neural networks in this case) learn via supervision, where raw images and binary masks for each instance of interest are shown to the network during training time. For example, in the TensorFlow Object Detection API, along with raw images, a list of binary masks, a list of corresponding bounding boxes (BBs), and a corresponding list of classification IDs form the training set, as shown below pictorially.
A successful outcome of the learning process is a well-trained model, evaluated on certain metrics. For Instance Segmentation tasks, Average Precision (AP) is commonly used. The AP is the integrated area under the Precision-Recall curve, which, in turn, is constructed by varying the Intersection-over-Union (IoU) threshold for binary mask pairs (Ground Truth, Predicted Mask). The following is an illustration of the evaluation metrics presented by TensorBoard.
After a successful learning process, the model can simply be presented a raw image and, from that raw image, generate a set of binary masks, along with a corresponding set of class labels and bounding boxes, where one mask is generated for each Region of Interest (illustrated below).
Following illustration shows the result of a trained model applying Instance Segmentation to an arbitrary urban driving scene.
This article discussed the autonomous driving pipeline with a focus on the Computer Vision aspects of it. Although much effort has gone into the algorithmic side of Computer Vision, training Deep Convolutional Neural Networks remains a big challenge due to computational demands of training large, complex networks. Most of high performing networks have from hundreds of millions to a few billions parameters. It takes an enormous amount of computational resources to train these parameters. Further, the sample complexity of these algorithms require a large amounts of data, on the order of millions of images, in order to achieve acceptable performance.
Brightics Deep Learning platform is the perfect tool to perform instance segmentation training tasks in a distributed manner. It provides the best of Deep Learning (Distributed Keras and Distributed Tensorflow), accessible through a familiar Jupyter environment and does not require any low-level programming of Docker, Kubernetes and Cluster programming. In addition, tasks like sensor fusion are achieved on a cluster with underlying Apache Spark technology, without any spark programming knowledge.
Brightics Deep Learning platform provides AI Governance, GPU Consolidation, Distributed Data Transformation, Automated Experiments with Hyper-Parameter Search, Distributed Inference in a Jupyter Notebook Environment familiar to Data Scientists.
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Yogesh develops AI applications and systems with particular focus on large scale Deep Learning tasks.
In past, he worked on developing and deploying large scale Machine Learning systems for Bitcoin and Ethereum blockchain analytics solutions.