Deep Learning for Autonomous Vehicles: Predictive Models

This lecture covers the topic of predictive models and trackers in the context of deep learning for autonomous vehicles. It delves into the challenges of object detection, tracking, and multi-target tracking, discussing various algorithms and strategies. The instructor presents the formulation of single and multi-target tracking problems, along with the mathematical definitions and optimization techniques involved. Additionally, the lecture explores the use of neural networks for tracking, the importance of object representation, and the advancements in learning to track with recurrent neural networks. The session concludes with insights into 3D pedestrian localization, uncertainty estimation, and the application of social cues for tasks like social distancing.

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CIVIL-459: Deep learning for autonomous vehicles

Deep Learning (DL) is the subset of Machine learning reshaping the future of transportation and mobility. In this class, we will show how DL can be used to teach autonomous vehicles to detect objects,

Machine learning

Machine learning (ML) is an umbrella term for solving problems for which development of algorithms by human programmers would be cost-prohibitive, and instead the problems are solved by helping machines 'discover' their 'own' algorithms, without needing to be explicitly told what to do by any human-developed algorithms. Recently, generative artificial neural networks have been able to surpass results of many previous approaches.

Vehicular automation

Vehicular automation involves the use of mechatronics, artificial intelligence, and multi-agent systems to assist the operator of a vehicle (car, aircraft, watercraft, or otherwise). These features and the vehicles employing them may be labeled as intelligent or smart. A vehicle using automation for difficult tasks, especially navigation, to ease but not entirely replace human input, may be referred to as semi-autonomous, whereas a vehicle relying solely on automation is called robotic or autonomous.

Lethal autonomous weapon

Lethal autonomous weapons (LAWs) are a type of autonomous military system that can independently search for and engage targets based on programmed constraints and descriptions. LAWs are also known as lethal autonomous weapon systems (LAWS), autonomous weapon systems (AWS), robotic weapons or killer robots. LAWs may operate in the air, on land, on water, underwater, or in space. The autonomy of current systems was restricted in the sense that a human gives the final command to attack—though there are exceptions with certain "defensive" systems.

Unmanned ground vehicle

An unmanned ground vehicle (UGV) is a vehicle that operates while in contact with the ground and without an onboard human presence. UGVs can be used for many applications where it may be inconvenient, dangerous, or impossible to have a human operator present. Generally, the vehicle will have a set of sensors to observe the environment, and will either autonomously make decisions about its behavior or pass the information to a human operator at a different location who will control the vehicle through teleoperation.

Recurrent neural network

A recurrent neural network (RNN) is one of the two broad types of artificial neural network, characterized by direction of the flow of information between its layers. In contrast to uni-directional feedforward neural network, it is a bi-directional artificial neural network, meaning that it allows the output from some nodes to affect subsequent input to the same nodes. Their ability to use internal state (memory) to process arbitrary sequences of inputs makes them applicable to tasks such as unsegmented, connected handwriting recognition or speech recognition.

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Explores trajectory forecasting in autonomous vehicles, focusing on deep learning models for predicting human trajectories in socially-aware transportation scenarios.

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Explores neural networks optimization, including backpropagation, batch normalization, weight initialization, and hyperparameter search strategies.

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