A Deep Learning Framework for Generation and Analysis of Driving Scenario Trajectories
Journal article, 2023

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.

Time series analysis


Autonomous drive safety verification

Generative Adversarial Networks (GANs)

Outlier detection


Andreas Demetriou

Student at Chalmers

Henrik Alfsvåg

Student at Chalmers

Sadegh Rahrovani


Morteza Haghir Chehreghani

Chalmers, Computer Science and Engineering (Chalmers), Data Science and AI

SN Computer Science

2662995X (ISSN) 26618907 (eISSN)

Vol. 4 3 251

Subject Categories

Bioinformatics (Computational Biology)


Computer Vision and Robotics (Autonomous Systems)



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