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Found 9 papers, 2 papers with code
Lift-Attend-Splat: Bird's-eye-view camera-lidar fusion using transformers
Combining complementary sensor modalities is crucial to providing robust perception for safety-critical robotics applications such as autonomous driving (AD).
Attacking Motion Planners Using Adversarial Perception Errors
Autonomous driving (AD) systems are often built and tested in a modular fashion, where the performance of different modules is measured using task-specific metrics.
Comparison of Pedestrian Prediction Models from Trajectory and Appearance Data for Autonomous Driving
Typical predictors use the trajectory history to predict future motion, however in cases of motion initiation, motion in the trajectory may only be clearly visible after a delay, which can result in the pedestrian has entered the road area before an accurate prediction can be made.
An Active Learning Reliability Method for Systems with Partially Defined Performance Functions
This enables active learning Gaussian process methods to be applied to problems where the performance of the system is sometimes undefined, and we demonstrate the effectiveness of our approach by testing our methodology on synthetic numerical examples for the autonomous driving domain.
Query-based Hard-Image Retrieval for Object Detection at Test Time
There is a longstanding interest in capturing the error behaviour of object detectors by finding images where their performance is likely to be unsatisfactory.
Beyond RMSE: Do machine-learned models of road user interaction produce human-like behavior?
Even though the models' RMSE value differed, all the models captured the kinematic-dependent merging behavior but struggled at varying degrees to capture the more nuanced courtesy lane change and highway lane change behavior.
A Step Towards Efficient Evaluation of Complex Perception Tasks in Simulation
There has been increasing interest in characterising the error behaviour of systems which contain deep learning models before deploying them into any safety-critical scenario.
Imagining the Unseen: Learning a Distribution over Incomplete Images with Dense Latent Trees
Images are composed as a hierarchy of object parts.
A Dataset for Lane Instance Segmentation in Urban Environments
Autonomous vehicles require knowledge of the surrounding road layout, which can be predicted by state-of-the-art CNNs.
