Towards Selection and Transition Between Behavior-Based Neural Networks for Automated Driving

TL;DR

This paper introduces a Behavior Selector system that dynamically chooses among multiple smaller neural networks for different driving tasks, enhancing interpretability and safety in autonomous vehicles.

Contribution

It proposes a novel method for selecting and transitioning between behavior-based neural networks in real time for autonomous driving.

Findings

Effective behavior selection demonstrated in simulation

Smooth transition between driving behaviors achieved

Improved safety and interpretability of autonomous systems

Abstract

Autonomous driving technology is progressing rapidly, largely due to complex End To End systems based on deep neural networks. While these systems are effective, their complexity can make it difficult to understand their behavior, raising safety concerns. This paper presents a new solution a Behavior Selector that uses multiple smaller artificial neural networks (ANNs) to manage different driving tasks, such as lane following and turning. Rather than relying on a single large network, which can be burdensome, require extensive training data, and is hard to understand, the developed approach allows the system to dynamically select the appropriate neural network for each specific behavior (e.g., turns) in real time. We focus on ensuring smooth transitions between behaviors while considering the vehicles current speed and orientation to improve stability and safety. The proposed system has been tested using the AirSim simulation environment, demonstrating its effectiveness.