# Data Driven Prediction Architecture for Autonomous Driving and its Application on Apollo Platform

arXiv: https://arxiv.org/abs/2006.06715 · HTML: https://mlsanity.com/paper/2006.06715/

## Key facts

- Cited by 7 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 11 Jun 2020.

## Built on by (most cited first, 7 of 7)

- [KoMA: Knowledge-driven Multi-agent Framework for Autonomous Driving with Large Language Models](https://mlsanity.com/paper/2407.14239/) (2024)
- [End-to-end Uncertainty-based Mitigation of Adversarial Attacks to Automated Lane Centering](https://mlsanity.com/paper/2103.00345/) (2021)
- [MS-Net: A Multi-Path Sparse Model for Motion Prediction in Multi-Scenes](https://mlsanity.com/paper/2403.00353/) (2024)
- [How Does Traffic Environment Quantitatively Affect the Autonomous Driving Prediction?](https://mlsanity.com/paper/2301.04414/) (2023)
- [Open-set Intersection Intention Prediction for Autonomous Driving](https://mlsanity.com/paper/2103.00140/) (2021)
- [A Certifiable Security Patch for Object Tracking in Self-Driving Systems via Historical Deviation Modeling](https://mlsanity.com/paper/2207.08556/) (2022)
- [A LiDAR Assisted Control Module with High Precision in Parking Scenarios for Autonomous Driving Vehicle](https://mlsanity.com/paper/2105.00398/) (2021)

## Abstract (arXiv)

Autonomous Driving vehicles (ADV) are on road with large scales. For safe and efficient operations, ADVs must be able to predict the future states and iterative with road entities in complex, real-world driving scenarios. How to migrate a well-trained prediction model from one geo-fenced area to another is essential in scaling the ADV operation and is difficult most of the time since the terrains, traffic rules, entities distributions, driving/walking patterns would be largely different in different geo-fenced operation areas. In this paper, we introduce a highly automated learning-based prediction model pipeline, which has been deployed on Baidu Apollo self-driving platform, to support different prediction learning sub-modules' data annotation, feature extraction, model training/tuning and deployment. This pipeline is completely automatic without any human intervention and shows an up to 400% efficiency increase in parameter tuning, when deployed at scale in different scenarios across nations.