# A Deep Learning Framework for Generation and Analysis of Driving Scenario Trajectories

arXiv: https://arxiv.org/abs/2007.14524 · HTML: https://mlsanity.com/paper/2007.14524/

## Key facts

- Cited by 12 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 28 Jul 2020.

## Built on by (most cited first, 12 of 12)

- [Active Learning of Driving Scenario Trajectories](https://mlsanity.com/paper/2108.03217/) (2021)
- [Leveraging Generative AI for Urban Digital Twins: A Scoping Review on the Autonomous Generation of Urban Data, Scenarios, Designs, and 3D City Models for Smart City Advancement](https://mlsanity.com/paper/2405.19464/) (2024)
- [Novelty Detection and Analysis of Traffic Scenario Infrastructures in the Latent Space of a Vision Transformer-Based Triplet Autoencoder](https://mlsanity.com/paper/2105.01924/) (2021)
- [Shift of Pairwise Similarities for Data Clustering](https://mlsanity.com/paper/2110.13103/) (2021)
- [Analyzing and Enhancing Closed-loop Stability in Reactive Simulation](https://mlsanity.com/paper/2208.04559/) (2022)
- [Traffic Scenario Clustering by Iterative Optimisation of Self-Supervised Networks Using a Random Forest Activation Pattern Similarity](https://mlsanity.com/paper/2105.07639/) (2021)
- [AirTrafficGen: Configurable Air Traffic Scenario Generation with Large Language Models](https://mlsanity.com/paper/2508.02269/) (2025)
- [Generative AI for Testing of Autonomous Driving Systems: A Survey](https://mlsanity.com/paper/2508.19882/) (2025)
- [Expert-LaSTS: Expert-Knowledge Guided Latent Space for Traffic Scenarios](https://mlsanity.com/paper/2207.09120/) (2022)
- [Passive and Active Learning of Driver Behavior from Electric Vehicles](https://mlsanity.com/paper/2203.02179/) (2022)
- [Quantitative Representation of Scenario Difficulty for Autonomous Driving Based on Adversarial Policy Search](https://mlsanity.com/paper/2408.14000/) (2024)
- [Emergency Lane-Change Simulation: A Behavioral Guidance Approach for Risky Scenario Generation](https://mlsanity.com/paper/2603.20234/) (2026)

## Abstract (arXiv)

We propose a unified deep learning framework for the generation and analysis of driving scenario trajectories, and validate its effectiveness in a principled way. To model and generate scenarios of trajectories with different lengths, we develop two approaches. First, we adapt the Recurrent Conditional Generative Adversarial Networks (RC-GAN) by conditioning on the length of the trajectories. This provides us the flexibility to generate variable-length driving trajectories, a desirable feature for scenario test case generation in the verification of autonomous driving. Second, we develop an architecture based on Recurrent Autoencoder with GANs to obviate the variable length issue, wherein we train a GAN to learn/generate the latent representations of original trajectories. In this approach, we train an integrated feed-forward neural network to estimate the length of the trajectories to be able to bring them back from the latent space representation. In addition to trajectory generation, we employ the trained autoencoder as a feature extractor, for the purpose of clustering and anomaly detection, to obtain further insights into the collected scenario dataset. We experimentally investigate the performance of the proposed framework on real-world scenario trajectories obtained from in-field data collection.