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Simulation-based reinforcement learning for real-world autonomous driving
We use synthetic data and a reinforcement learning algorithm to train a ...
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Grounded Relational Inference: Domain Knowledge Driven Explainable Autonomous Driving
Explainability is essential for autonomous vehicles and other robotics s...
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Fine-Grained Vehicle Perception via 3D Part-Guided Visual Data Augmentation
Holistically understanding an object and its 3D movable parts through vi...
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Driving Datasets Literature Review
This report is a survey of the different autonomous driving datasets whi...
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Data-Driven Distributed State Estimation and Behavior Modeling in Sensor Networks
Nowadays, the prevalence of sensor networks has enabled tracking of the ...
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Teaching Autonomous Driving Using a Modular and Integrated Approach
Autonomous driving is not one single technology but rather a complex sys...
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BLVD: Building A Large-scale 5D Semantics Benchmark for Autonomous Driving
In autonomous driving community, numerous benchmarks have been establish...
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A Customizable Dynamic Scenario Modeling and Data Generation Platform for Autonomous Driving
Safely interacting with humans is a significant challenge for autonomous driving. The performance of this interaction depends on machine learning-based modules of an autopilot, such as perception, behavior prediction, and planning. These modules require training datasets with high-quality labels and a diverse range of realistic dynamic behaviors. Consequently, training such modules to handle rare scenarios is difficult because they are, by definition, rarely represented in real-world datasets. Hence, there is a practical need to augment datasets with synthetic data covering these rare scenarios. In this paper, we present a platform to model dynamic and interactive scenarios, generate the scenarios in simulation with different modalities of labeled sensor data, and collect this information for data augmentation. To our knowledge, this is the first integrated platform for these tasks specialized to the autonomous driving domain.
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