Logo
ScienceStack
Table of Contents
Fetching ...
Sign In

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

Iqra Aslam, Igor Anpilogov, Andreas Rausch

TL;DR

The paper tackles safety and interpretability concerns of End-to-End autonomous driving by proposing a Behavior Selector that manages multiple behavior-specific neural networks (e.g., lane following, turns) and selects appropriate behavior in real time guided by route planner commands. It introduces four strategies—Basic, Transition, Interpolation, and a Transition+Interpolation hybrid—to enable smooth, state-aware transitions between behaviors. Evaluation in a simulated Unreal Engine AirSim environment shows that interpolation provides the most stable speed control with no transition spikes, while the transition-based approach offers simplicity but can suffer from momentum-related transients; the hybrid approach yields mixed results and higher computational costs. Overall, the work advances safe, modular autonomous driving by enabling dynamic, state-consistent switching between specialized networks and lays groundwork for real-world validation and further optimization.

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.

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

TL;DR

The paper tackles safety and interpretability concerns of End-to-End autonomous driving by proposing a Behavior Selector that manages multiple behavior-specific neural networks (e.g., lane following, turns) and selects appropriate behavior in real time guided by route planner commands. It introduces four strategies—Basic, Transition, Interpolation, and a Transition+Interpolation hybrid—to enable smooth, state-aware transitions between behaviors. Evaluation in a simulated Unreal Engine AirSim environment shows that interpolation provides the most stable speed control with no transition spikes, while the transition-based approach offers simplicity but can suffer from momentum-related transients; the hybrid approach yields mixed results and higher computational costs. Overall, the work advances safe, modular autonomous driving by enabling dynamic, state-consistent switching between specialized networks and lays groundwork for real-world validation and further optimization.

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.

Paper Structure

This paper contains 19 sections, 2 equations, 8 figures.

Table of Contents

  1. Introduction
  2. Related Work
  3. Driving behavior selector: system Architecture and concept:
  4. Basic Behavior Selector (no transition and no interpolation):
  5. Transition-Based Behavior Selector:
  6. Interpolation-Based Behavior Selector
  7. Interpolation Distance based coefficient
  8. Interpolation Distance based coefficient
  9. Behavior Selector based on Transition and interpolation
  10. Evaluation and Results:
  11. Evaluation Environment:
  12. Driving Behavior networks
  13. Discussion of results
  14. Basic Behavior Selector
  15. Transition based behavior selector
  16. ...and 4 more sections

Figures (8)

  • Figure 1: Architecture for a behavior-based E2E autonomous driving system
  • Figure 2: Depiction of the turn (green color) and transition (orange color) distance
  • Figure 3: Interpolation Distance based coefficient and Turn Distance based coefficient
  • Figure 4: Bird view of test track and the vehicle
  • Figure 5: Squared error between predicted and actual speed over time for Basic Behavior Selector
  • ...and 3 more figures