Visual trajectory prediction for autonomous vehicles

Project Motivation

Accurate and reliable trajectory estimation is a critical component of autonomous vehicle technology. It enables self-driving cars to make informed decisions about how to navigate their environment and avoid collisions with other objects. Traditional methods for trajectory estimation rely on sensors such as radar and lidar, which can be expensive and bulky. In contrast, direct vision-based methods use cameras and computer vision algorithms to estimate the motion of objects in the environment. These methods are more lightweight and scalable, making them a promising approach for enabling autonomous vehicles to navigate safely and efficiently. Additionally, direct vision-based methods have the potential to provide a more intuitive and human-like understanding of the environment, as they use the same visual information that a human driver would use to make decisions. Overall, the motivation for developing direct vision-based trajectory estimation methods for autonomous vehicles is to enable more affordable and effective navigation in complex and dynamic environments. 

Project Description

Autonomous vehicles rely on accurate and reliable trajectory prediction to navigate safely and efficiently. Visual trajectory prediction, which uses cameras and computer vision algorithms to predict the future motion of objects in the environment, is a key technology for enabling self-driving cars to make informed decisions and avoid collisions. In this work, we present a new approach for visual trajectory prediction that achieves state-of-the-art performance and offers significant improvements over existing methods. Our approach uses deep learning techniques to learn the complex patterns and relationships in visual data, allowing it to make more accurate and reliable predictions about the future motion of objects in the environment. With our method, autonomous vehicles can better anticipate the behaviour of other objects on the road, enabling them to make safer and more efficient driving decisions. 

Status: Ongoing

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