Multimodal Trajectory Predictions for Autonomous Driving using Deep Convolutional Networks

09/18/2018
by   Henggang Cui, et al.
0

Autonomous driving presents one of the largest problems that the robotics and artificial intelligence communities are facing at the moment, both in terms of difficulty and potential societal impact. Self-driving vehicles (SDVs) are expected to prevent road accidents and save millions of lives while improving the livelihood and life quality of many more. However, despite large interest and a number of industry players working in the autonomous domain, there is still more to be done in order to develop a system capable of operating at a level comparable to best human drivers. One reason for this is high uncertainty of traffic behavior and large number of situations that an SDV may encounter on the roads, making it very difficult to create a fully generalizable system. To ensure safe and efficient operations, an autonomous vehicle is required to account for this uncertainty and to anticipate a multitude of possible behaviors of traffic actors in its surrounding. In this work, we address this critical problem and present a method to predict multiple possible trajectories of actors while also estimating their probabilities. The method encodes each actor's surrounding context into a raster image, used as input by deep convolutional networks to automatically derive relevant features for the task. Following extensive offline evaluation and comparison to state-of-the-art baselines, as well as closed course tests, the method was successfully deployed to a fleet of SDVs.

READ FULL TEXT
research
08/17/2018

Short-term Motion Prediction of Traffic Actors for Autonomous Driving using Deep Convolutional Networks

Despite its ubiquity in our daily lives, AI is only just starting to mak...
research
08/17/2018

Motion Prediction of Traffic Actors for Autonomous Driving using Deep Convolutional Networks

Recent algorithmic improvements and hardware breakthroughs resulted in a...
research
11/05/2020

Ellipse Loss for Scene-Compliant Motion Prediction

Motion prediction is a critical part of self-driving technology, respons...
research
03/29/2023

Decision Making for Autonomous Driving in Interactive Merge Scenarios via Learning-based Prediction

Autonomous agents that drive on roads shared with human drivers must rea...
research
05/16/2023

Self-Aware Trajectory Prediction for Safe Autonomous Driving

Trajectory prediction is one of the key components of the autonomous dri...
research
01/11/2023

How Does Traffic Environment Quantitatively Affect the Autonomous Driving Prediction?

An accurate trajectory prediction is crucial for safe and efficient auto...
research
05/24/2021

Fixed-Dimensional and Permutation Invariant State Representation of Autonomous Driving

In this paper, we propose a new state representation method, called enco...

Please sign up or login with your details

Forgot password? Click here to reset