# Machine Learning-Based Vehicle Intention Trajectory Recognition and Prediction for Autonomous Driving

arXiv: https://arxiv.org/abs/2402.16036 · HTML: https://mlsanity.com/paper/2402.16036/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- Builds on 3 of its 31 references that are in the map.
- First posted on arXiv 25 Feb 2024.

## Builds on (most cited first, 3 of 3)

- [Particle Filter SLAM for Vehicle Localization](https://mlsanity.com/paper/2402.07429/) (2024)
- [Progress in artificial intelligence applications based on the combination of self-driven sensors and deep learning](https://mlsanity.com/paper/2402.09442/) (2024)
- [Construction and application of artificial intelligence crowdsourcing map based on multi-track GPS data](https://mlsanity.com/paper/2402.15796/) (2024)

## Built on by (most cited first, 4 of 4)

- [Maximizing User Experience with LLMOps-Driven Personalized Recommendation Systems](https://mlsanity.com/paper/2404.00903/) (2024)
- [Privacy Leaks by Adversaries: Adversarial Iterations for Membership Inference Attack](https://mlsanity.com/paper/2506.02711/) (2025)
- [Evaluating Scenario-based Decision-making for Interactive Autonomous Driving Using Rational Criteria: A Survey](https://mlsanity.com/paper/2501.01886/) (2025)
- [Practical Applications of Advanced Cloud Services and Generative AI Systems in Medical Image Analysis](https://mlsanity.com/paper/2403.17549/) (2024)

## Abstract (arXiv)

In recent years, the expansion of internet technology and advancements in automation have brought significant attention to autonomous driving technology. Major automobile manufacturers, including Volvo, Mercedes-Benz, and Tesla, have progressively introduced products ranging from assisted-driving vehicles to semi-autonomous vehicles. However, this period has also witnessed several traffic safety incidents involving self-driving vehicles. For instance, in March 2016, a Google self-driving car was involved in a minor collision with a bus. At the time of the accident, the autonomous vehicle was attempting to merge into the right lane but failed to dynamically respond to the real-time environmental information during the lane change. It incorrectly assumed that the approaching bus would slow down to avoid it, leading to a low-speed collision with the bus. This incident highlights the current technological shortcomings and safety concerns associated with autonomous lane-changing behavior, despite the rapid advancements in autonomous driving technology. Lane-changing is among the most common and hazardous behaviors in highway driving, significantly impacting traffic safety and flow. Therefore, lane-changing is crucial for traffic safety, and accurately predicting drivers' lane change intentions can markedly enhance driving safety. This paper introduces a deep learning-based prediction method for autonomous driving lane change behavior, aiming to facilitate safe lane changes and thereby improve road safety.