Trajectory Forecasting in Autonomous Vehicles

This lecture by the instructor covers the topic of trajectory forecasting in autonomous vehicles, focusing on deep learning models for predicting human trajectories in socially-aware transportation scenarios. The lecture delves into the challenges of sequence modelling and encoding social interactions, presenting the TrajNet++ benchmark for evaluating forecasting models. It discusses the types of trajectories, the importance of understanding complex scenarios, and the different categories of human interactions in pedestrian forecasting. The lecture also explores grid-based and non-grid-based interaction modules, attention mechanisms, and the use of transformer networks in trajectory forecasting.

About this result

This page is automatically generated and may contain information that is not correct, complete, up-to-date, or relevant to your search query. The same applies to every other page on this website. Please make sure to verify the information with EPFL's official sources.

Related lectures (408)

Related concepts (252)

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,

Autonomous robot

An autonomous robot is a robot that acts without recourse to human control. The first autonomous robots environment were known as Elmer and Elsie, which were constructed in the late 1940s by W. Grey Walter. They were the first robots in history that were programmed to "think" the way biological brains do and meant to have free will. Elmer and Elsie were often labeled as tortoises because of how they were shaped and the manner in which they moved. They were capable of phototaxis which is the movement that occurs in response to light stimulus.

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.

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.

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.

Uncrewed vehicle

An uncrewed vehicle or unmanned vehicle is a vehicle without a person on board. Uncrewed vehicles can either be under telerobotic control—remote controlled or remote guided vehicles—or they can be autonomously controlled—autonomous vehicles—which are capable of sensing their environment and navigating on their own. There are different types of uncrewed vehicles: Remote control vehicle (RC), such as radio-controlled cars or radio-controlled aircraft Unmanned ground vehicle (UGV), such as the autonomous cars

Self-supervised Learning for Autonomous Vehicles CIVIL-459: Deep learning for autonomous vehicles

Explores self-supervised learning for autonomous vehicles, deriving labels from data itself and discussing its applications and challenges.

Autonomous Vehicles: Trajectory Prediction and Social Behavior CIVIL-459: Deep learning for autonomous vehicles

Explores challenges in deep learning for autonomous vehicles, emphasizing social behavior modeling and feasible trajectory prediction.

Generative Models: Trajectory Forecasting CIVIL-459: Deep learning for autonomous vehicles

Explores generative models for trajectory forecasting in autonomous vehicles, including discriminative vs generative models, VAES, GANS, and case studies.

Neural Networks Optimization CIVIL-459: Deep learning for autonomous vehicles

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

Perception: Data-Driven Approaches CIVIL-459: Deep learning for autonomous vehicles

Explores perception in deep learning for autonomous vehicles, covering image classification, optimization methods, and the role of representation in machine learning.