Data generation for connected and automated vehicle tests using deep learning models.

Area of Science:

• Artificial Intelligence
• Robotics
• Transportation Engineering

Background:

• Connected and automated vehicles (CAVs) require extensive testing for safety validation.
• Real-world trajectory data for CAVs is often limited in sample size and diversity.
• Critical scenarios crucial for CAV testing may be absent in collected datasets.

Purpose of the Study:

• To develop advanced generative models for creating realistic and diverse background vehicle trajectory data.
• To evaluate the effectiveness of generated trajectory data in improving CAV safety performance assessment.
• To compare the performance of Wasserstein generative adversarial network with gradient penalty (WGAN-GP) and variational autoencoder-generative adversarial network (VAE-GAN) models.

Main Methods:

• Developed and implemented WGAN-GP and VAE-GAN models for trajectory data generation.
• Learned compressed representations of trajectory data in latent space.
• Generated new trajectory data by sampling from the latent space and mapping back.
• Integrated real and generated data into a cooperative adaptive cruise control (CACC) car-following model for CAVs.
• Evaluated safety performance using the time-to-collision (TTC) index.

Main Results:

• Generated trajectory data exhibited both differences and similarities to real data samples.
• The use of generated trajectory data increased the occurrence of critical fragments (low TTC) in CAV simulations.
• WGAN-GP demonstrated superior performance over VAE-GAN in generating critical fragments, indicated by a higher ratio.
• Both models successfully expanded the diversity of trajectory data for testing.

Conclusions:

• Generative models like WGAN-GP and VAE-GAN are effective for augmenting CAV trajectory datasets.
• Generated data, particularly from WGAN-GP, can reveal more critical safety scenarios for CAVs.
• This approach enhances the robustness and comprehensiveness of simulation-based testing for CAVs.
• Findings support improved safety performance evaluation and development of CAVs.