# Neuroevolutionary Multi-objective approaches to Trajectory Prediction in Autonomous Vehicles

arXiv: https://arxiv.org/abs/2205.02105 · HTML: https://mlsanity.com/paper/2205.02105/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- Builds on 12 of its 21 references that are in the map.
- First posted on arXiv 4 May 2022.

## Builds on (most cited first, 12 of 12)

- [OmniDet: Surround View Cameras based Multi-task Visual Perception Network for Autonomous Driving](https://mlsanity.com/paper/2102.07448/) (2021)
- [SynDistNet: Self-Supervised Monocular Fisheye Camera Distance Estimation Synergized with Semantic Segmentation for Autonomous Driving](https://mlsanity.com/paper/2008.04017/) (2020)
- [Neuroevolution in Deep Neural Networks: Current Trends and Future Challenges](https://mlsanity.com/paper/2006.05415/) (2020)
- [UnRectDepthNet: Self-Supervised Monocular Depth Estimation using a Generic Framework for Handling Common Camera Distortion Models](https://mlsanity.com/paper/2007.06676/) (2020)
- [SVDistNet: Self-Supervised Near-Field Distance Estimation on Surround View Fisheye Cameras](https://mlsanity.com/paper/2104.04420/) (2021)
- [Congestion-aware Multi-agent Trajectory Prediction for Collision Avoidance](https://mlsanity.com/paper/2103.14231/) (2021)
- [AuxNet: Auxiliary tasks enhanced Semantic Segmentation for Automated Driving](https://mlsanity.com/paper/1901.05808/) (2019)
- [PLOP: Probabilistic poLynomial Objects trajectory Planning for autonomous driving](https://mlsanity.com/paper/2003.08744/) (2020)
- [Interaction-Aware Trajectory Prediction of Connected Vehicles using CNN-LSTM Networks](https://mlsanity.com/paper/2005.12134/) (2020)
- [NeuroTrajectory: A Neuroevolutionary Approach to Local State Trajectory Learning for Autonomous Vehicles](https://mlsanity.com/paper/1906.10971/) (2019)
- [GridSim: A Vehicle Kinematics Engine for Deep Neuroevolutionary Control in Autonomous Driving](https://mlsanity.com/paper/1901.05195/) (2019)
- [Neuroevolution in Deep Learning: The Role of Neutrality](https://mlsanity.com/paper/2102.08475/) (2021)

## Built on by (most cited first, 5 of 5)

- [Bi-level Multi-objective Evolutionary Learning: A Case Study on Multi-task Graph Neural Topology Search](https://mlsanity.com/paper/2302.02565/) (2023)
- [NeuroLGP-SM: A Surrogate-assisted Neuroevolution Approach using Linear Genetic Programming](https://mlsanity.com/paper/2403.19459/) (2024)
- [FisheyeDetNet: 360deg Surround view Fisheye Camera based Object Detection System for Autonomous Driving](https://mlsanity.com/paper/2404.13443/) (2024)
- [NeuroLGP-SM: Scalable Surrogate-Assisted Neuroevolution for Deep Neural Networks](https://mlsanity.com/paper/2404.08786/) (2024)
- [NEAT-NC: NEAT guided Navigation Cells for Robot Path Planning](https://mlsanity.com/paper/2604.15076/) (2026)

## Abstract (arXiv)

The incentive for using Evolutionary Algorithms (EAs) for the automated optimization and training of deep neural networks (DNNs), a process referred to as neuroevolution, has gained momentum in recent years. The configuration and training of these networks can be posed as optimization problems. Indeed, most of the recent works on neuroevolution have focused their attention on single-objective optimization. Moreover, from the little research that has been done at the intersection of neuroevolution and evolutionary multi-objective optimization (EMO), all the research that has been carried out has focused predominantly on the use of one type of DNN: convolutional neural networks (CNNs), using well-established standard benchmark problems such as MNIST. In this work, we make a leap in the understanding of these two areas (neuroevolution and EMO), regarded in this work as neuroevolutionary multi-objective, by using and studying a rich DNN composed of a CNN and Long-short Term Memory network. Moreover, we use a robust and challenging vehicle trajectory prediction problem. By using the well-known Non-dominated Sorting Genetic Algorithm-II, we study the effects of five different objectives, tested in categories of three, allowing us to show how these objectives have either a positive or detrimental effect in neuroevolution for trajectory prediction in autonomous vehicles.