Data Augmentation and Generation for Improving Learning-based Autonomous Driving Models
Abstract
Learning-based models have evolved to become the main-stream in autonomous driving research and industry recent years, where data serve an irreplaceable role. However, real-world driving data are costly, requiring expensive data collection instruments and substantial human labors. Therefore, there is a critical need to maximize the informational yield of available datasets and investigate synthetic data generation as a scalable alternative, in order to enable the scaling of learning-based driving models while circumventing the prohibitive costs associated with massive real-world data acquisition.In this thesis, we first introduce heuristic approaches for the direct augmentation and synthesis of driving data in Chapter 2, aiming to empirically validate the significance of data scaling for autonomous driving models. Specifically, we employ prescriptive planning models to synthesize physically plausible vehicle trajectories and apply geometric transformations to augment existing map topologies. These operations, while simple enough and involve no additional data collections, introduce critical diversity to the motion data, both for the trajectory side and for the map side. By using these augmented data to pre-train a motion prediction model, we observe substantial gains in the motion prediction accuracy.Beyond direct geometric augmentations, enriching the semantic modalities of existing datasets serves as a critical strategy to extract deeper insights from available driving…
Read the paper
Related studies
- Learning from First-Generation Qualitative Approaches in the IS Discipline: An Evolutionary View and Some Implications for Authors and Evaluators (PART 1/2)
- Commentary on the clinical and preclinical dosage limits of interstitially administered magnetic fluids for therapeutic hyperthermia based on current practice and efficacy models
- Prosthesis: New Technological Opportunities and Innovative Biomedical Devices
- The history and future of neural modeling for cochlear implants
- Ryanodine receptors: waking up from refractoriness
- Novel Transgenic Rabbit Model Sheds Light on the Puzzling Role of Matrix Metalloproteinase-12 in Atherosclerosis
Go deeper in Evidence Hub
Evidence Hub adds a study data sheet (design, sample size, evidence level, key numbers), an AI summary and a chat grounded in the paper's text.