# Towards Safe and Reliable Autonomous Driving: Dynamic Occupancy Set Prediction

arXiv: https://arxiv.org/abs/2402.19385 · HTML: https://mlsanity.com/paper/2402.19385/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 17 of its 31 references that are in the map.
- First posted on arXiv 29 Feb 2024.

## Builds on (most cited first, 17 of 17)

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- [ADBench: Anomaly Detection Benchmark](https://mlsanity.com/paper/2206.09426/) (2022)
- [Anomaly Detection in Autonomous Driving: A Survey](https://mlsanity.com/paper/2204.07974/) (2022)
- [Adaptive Trajectory Prediction via Transferable GNN](https://mlsanity.com/paper/2203.05046/) (2022)
- [Conformal Prediction for STL Runtime Verification](https://mlsanity.com/paper/2211.01539/) (2022)
- [SIND: A Drone Dataset at Signalized Intersection in China](https://mlsanity.com/paper/2209.02297/) (2022)
- [Machine Learning for Autonomous Vehicle's Trajectory Prediction: A comprehensive survey, Challenges, and Future Research Directions](https://mlsanity.com/paper/2307.07527/) (2023)
- [Pedestrian Trajectory Prediction with Convolutional Neural Networks](https://mlsanity.com/paper/2010.05796/) (2020)
- [SSAGCN: Social Soft Attention Graph Convolution Network for Pedestrian Trajectory Prediction](https://mlsanity.com/paper/2112.02459/) (2021)
- [MTP-GO: Graph-Based Probabilistic Multi-Agent Trajectory Prediction with Neural ODEs](https://mlsanity.com/paper/2302.00735/) (2023)
- [An Ethical Trajectory Planning Algorithm for Autonomous Vehicles](https://mlsanity.com/paper/2212.08577/) (2022)
- [Physics Constrained Motion Prediction with Uncertainty Quantification](https://mlsanity.com/paper/2302.01060/) (2023)
- [See Yourself in Others: Attending Multiple Tasks for Own Failure Detection](https://mlsanity.com/paper/2110.02549/) (2021)
- [Self-Aware Trajectory Prediction for Safe Autonomous Driving](https://mlsanity.com/paper/2305.09147/) (2023)
- [Failure Detection for Motion Prediction of Autonomous Driving: An Uncertainty Perspective](https://mlsanity.com/paper/2301.04421/) (2023)
- [How Does Traffic Environment Quantitatively Affect the Autonomous Driving Prediction?](https://mlsanity.com/paper/2301.04414/) (2023)

## Abstract (arXiv)

In the rapidly evolving field of autonomous driving, reliable prediction is pivotal for vehicular safety. However, trajectory predictions often deviate from actual paths, particularly in complex and challenging environments, leading to significant errors. To address this issue, our study introduces a novel method for Dynamic Occupancy Set (DOS) prediction, it effectively combines advanced trajectory prediction networks with a DOS prediction module, overcoming the shortcomings of existing models. It provides a comprehensive and adaptable framework for predicting the potential occupancy sets of traffic participants. The innovative contributions of this study include the development of a novel DOS prediction model specifically tailored for navigating complex scenarios, the introduction of precise DOS mathematical representations, and the formulation of optimized loss functions that collectively advance the safety and efficiency of autonomous systems. Through rigorous validation, our method demonstrates marked improvements over traditional models, establishing a new benchmark for safety and operational efficiency in intelligent transportation systems.