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

Abstract

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.

References in corpus (6)

• C-RNN-GAN: Continuous recurrent neural networks with adversarial training
• Real-valued (Medical) Time Series Generation with Recurrent Conditional GANs
• TimeNet: Pre-trained deep recurrent neural network for time series classification
• m-TSNE: A Framework for Visualizing High-Dimensional Multivariate Time Series
• HexaGAN: Generative Adversarial Nets for Real World Classification
• A Driving Intention Prediction Method Based on Hidden Markov Model for Autonomous Driving

Cited by in corpus (4)

• Novelty Detection and Analysis of Traffic Scenario Infrastructures in the Latent Space of a Vision Transformer-Based Triplet Autoencoder
• Passive and Active Learning of Driver Behavior from Electric Vehicles
• A Generic Framework for Clustering Vehicle Motion Trajectories
• Shift of Pairwise Similarities for Data Clustering