Situation Assessment for Planning Lane Changes: Combining Recurrent Models and Prediction

TL;DR

This paper introduces a deep learning-based situation assessment system for autonomous vehicles that classifies lane change scenarios by combining recurrent neural networks with predictive modeling, improving safety and decision-making.

Contribution

It presents a novel deep learning architecture integrating bidirectional RNNs with a predictive model for assessing lane change opportunities.

Findings

Outperforms existing methods on NGSIM datasets

Demonstrates effective classification of lane change scenarios

Validates the integration of prediction with recurrent models

Abstract

One of the greatest challenges towards fully autonomous cars is the understanding of complex and dynamic scenes. Such understanding is needed for planning of maneuvers, especially those that are particularly frequent such as lane changes. While in recent years advanced driver-assistance systems have made driving safer and more comfortable, these have mostly focused on car following scenarios, and less on maneuvers involving lane changes. In this work we propose a situation assessment algorithm for classifying driving situations with respect to their suitability for lane changing. For this, we propose a deep learning architecture based on a Bidirectional Recurrent Neural Network, which uses Long Short-Term Memory units, and integrates a prediction component in the form of the Intelligent Driver Model. We prove the feasibility of our algorithm on the publicly available NGSIM datasets, where we outperform existing methods.