Ethical Decision-making for Autonomous Driving based on LSTM Trajectory Prediction Network

TL;DR

This paper introduces an LSTM-based trajectory prediction network that incorporates ethical considerations into autonomous vehicle decision-making, enhancing safety and reliability in complex driving scenarios.

Contribution

It presents a novel method integrating ethical principles into trajectory prediction using LSTM with attention, improving decision-making in autonomous driving.

Findings

Enhanced trajectory prediction accuracy

Effective ethical decision-making demonstrated in simulations

Improved safety and reliability in autonomous vehicle trajectories

Abstract

The development of autonomous vehicles has brought a great impact and changes to the transportation industry, offering numerous benefits in terms of safety and efficiency. However, one of the key challenges that autonomous driving faces is how to make ethical decisions in complex situations. To address this issue, in this article, a novel trajectory prediction method is proposed to achieve ethical decision-making for autonomous driving. Ethical considerations are integrated into the decision-making process of autonomous vehicles by quantifying the utility principle and incorporating them into mathematical formulas. Furthermore, trajectory prediction is optimized using LSTM network with an attention module, resulting in improved accuracy and reliability in trajectory planning and selection. Through extensive simulation experiments, we demonstrate the effectiveness of the proposed method in making ethical decisions and selecting optimal trajectories.