Situation Assessment for Planning Lane Changes: Combining Recurrent Models and Prediction
This work addresses a critical challenge in autonomous driving by focusing on lane change maneuvers, which are frequent but less studied compared to car following scenarios, offering a domain-specific solution for safer and more efficient autonomous vehicle navigation.
The paper tackles the problem of assessing driving situations for lane change planning in autonomous vehicles by proposing a deep learning architecture that combines a Bidirectional Recurrent Neural Network with Long Short-Term Memory units and integrates the Intelligent Driver Model for prediction. The result is an algorithm that outperforms existing methods on the NGSIM datasets, demonstrating feasibility with improved performance metrics.
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.