Counterexample-Guided Safety Contracts for Autonomous Driving by DeCastro, Jonathan, Liebenwein, Lucas, Vasile, Cristian-Ioan, Tedrake, Russell L, Karaman, Sertac, Rus, Daniela L

“…However, formally verifying autonomous driving decisions systems is difficult. In this paper, we propose a frame-work for constructing a set of safety contracts that serve as design requirements for controller synthesis for a given scenario. …”
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Compositional and Contract-based Verification for Autonomous Driving on Road Networks by DeCastro, Jonathan, Alonso-Mora, Javier, Liebenwein, Lucas, Schwarting, Wilko, Vasile, Cristian-Ioan, Karaman, Sertac, Rus, Daniela L

“…Recent advances in autonomous driving have raised the problem of safety to the forefront and incentivized research into establishing safety guarantees. …”
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Variational Autoencoder for End-to-End Control of Autonomous Driving with Novelty Detection and Training De-biasing by Amini, Alexander, Araki, Brandon, Rus, Daniela, Schwarting, Wilko, Rosman, Guy, Karaman, Sertac, Rus, Daniela L

“…This paper introduces a new method for end-to-end training of deep neural networks (DNNs) and evaluates it in the context of autonomous driving. DNN training has been shown to result in high accuracy for perception to action learning given sufficient training data. …”
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Learning Robust Control Policies for End-to-End Autonomous Driving From Data-Driven Simulation by Amini, Alexander A, Gilitschenski, Igor, Phillips, Jacob, Moseyko, Julia, Banerjee, Rohan, Karaman, Sertac, Rus, Daniela L

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A General Pipeline for 3D Detection of Vehicles by Du, Xinxin, Ang, Marcelo H., Karaman, Sertac, Rus, Daniela

“…© 2018 IEEE. Autonomous driving requires 3D perception of vehicles and other objects in the in environment. …”
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Self-supervised sparse-to-dense: Self-supervised depth completion from LiDAR and monocular camera by Ma, Fangchang, Venturelli Cavalheiro, Guilherme., Karaman, Sertac

“…Depth completion, the technique of estimating a dense depth image from sparse depth measurements, has a variety of applications in robotics and autonomous driving. However, depth completion faces 3 main challenges: the irregularly spaced pattern in the sparse depth input, the difficulty in handling multiple sensor modalities (when color images are available), as well as the lack of dense, pixel-level ground truth depth labels for training. …”
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Perception-aware planning for differentially flat robots by Murali, Varun

“…With a camera, inertial measurement unit (IMU) pairing being ubiquitous to most consumer electronics, they form an ideal pairing for applications on the edge and have found applications ranging from large-scale search and rescue, autonomous driving to home robots such as robotic vacuum cleaners. …”
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Learning Steering Bounds for Parallel Autonomous Systems by Amini, Alexander A, Paull, Liam, Balch, Thomas M, Karaman, Sertac, Rus, Daniela L

“…Deep learning has been successfully applied to “end-to-end” learning of the autonomous driving task, where a deep neural network learns to predict steering control commands from camera data input. …”
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