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Found 32 papers, 20 papers with code
A Generalized Metriplectic System via Free Energy and System~Identification via Bilevel Convex Optimization
This work generalizes the classical metriplectic formalism to model Hamiltonian systems with nonconservative dissipation.
SPOT: Point Cloud Based Stereo Visual Place Recognition for Similar and Opposing Viewpoints
Recognizing places from an opposing viewpoint during a return trip is a common experience for human drivers.
Dataset and Benchmark: Novel Sensors for Autonomous Vehicle Perception
Conventional cameras employed in autonomous vehicle (AV) systems support many perception tasks, but are challenged by low-light or high dynamic range scenes, adverse weather, and fast motion.
Hyperspherical Embedding for Point Cloud Completion
Most real-world 3D measurements from depth sensors are incomplete, and to address this issue the point cloud completion task aims to predict the complete shapes of objects from partial observations.
Sense, Imagine, Act: Multimodal Perception Improves Model-Based Reinforcement Learning for Head-to-Head Autonomous Racing
As a result, MBRL agents may converge to sub-optimal policies if the world model is inaccurate.
LiSnowNet: Real-time Snow Removal for LiDAR Point Cloud
LiDARs have been widely adopted to modern self-driving vehicles, providing 3D information of the scene and surrounding objects.
CLONeR: Camera-Lidar Fusion for Occupancy Grid-aided Neural Representations
Recent advances in neural radiance fields (NeRFs) achieve state-of-the-art novel view synthesis and facilitate dense estimation of scene properties.
BiPOCO: Bi-Directional Trajectory Prediction with Pose Constraints for Pedestrian Anomaly Detection
We present BiPOCO, a Bi-directional trajectory predictor with POse COnstraints, for detecting anomalous activities of pedestrians in videos.
Ranked #6 on
Video Anomaly Detection
on HR-Avenue
Leveraging Trajectory Prediction for Pedestrian Video Anomaly Detection
Video anomaly detection is a core problem in vision.
Coupling Intent and Action for Pedestrian Crossing Behavior Prediction
In this work, we follow the neuroscience and psychological literature to define pedestrian crossing behavior as a combination of an unobserved inner will (a probabilistic representation of binary intent of crossing vs. not crossing) and a set of multi-class actions (e. g., walking, standing, etc.).
Learning Rotation-Invariant Representations of Point Clouds Using Aligned Edge Convolutional Neural Networks
Point cloud analysis is an area of increasing interest due to the development of 3D sensors that are able to rapidly measure the depth of scenes accurately.
BiTraP: Bi-directional Pedestrian Trajectory Prediction with Multi-modal Goal Estimation
BiTraP estimates the goal (end-point) of trajectories and introduces a novel bi-directional decoder to improve longer-term trajectory prediction accuracy.
Ranked #2 on
Trajectory Prediction
on JAAD
Point Set Voting for Partial Point Cloud Analysis
This paper illustrates that this proposed method achieves state-of-the-art performance on shape classification, part segmentation and point cloud completion.
Pixel-Wise Motion Deblurring of Thermal Videos
To address this problem, this paper formulates reversing the effect of thermal inertia at a single pixel as a Least Absolute Shrinkage and Selection Operator (LASSO) problem which we can solve rapidly using a quadratic programming solver.
Off The Beaten Sidewalk: Pedestrian Prediction In Shared Spaces For Autonomous Vehicles
Pedestrians and drivers interact closely in a wide range of environments.
Robotics
Covariance-Robust Dynamic Watermarking
Attack detection and mitigation strategies for cyberphysical systems (CPS) are an active area of research, and researchers have developed a variety of attack-detection tools such as dynamic watermarking.
Safe, Optimal, Real-time Trajectory Planning with a Parallel Constrained Bernstein Algorithm
To move through the world, mobile robots typically use a receding-horizon strategy, wherein they execute an old plan while computing a new plan to incorporate new sensor information.
Optimization and Control Robotics
Reachable Sets for Safe, Real-Time Manipulator Trajectory Design
At runtime, in each receding-horizon planning iteration, ARMTD constructs a reachable set of the entire arm in workspace and intersects it with obstacles to generate sub-differentiable and provably-conservative collision-avoidance constraints on the trajectory parameters.
Robotics
Shadow Transfer: Single Image Relighting For Urban Road Scenes
There have been impressive advances in the realm of image to image translation in transferring previously unseen visual effects into a dataset, specifically in day to night translation.
On-Demand Trajectory Predictions for Interaction Aware Highway Driving
Highway driving places significant demands on human drivers and autonomous vehicles (AVs) alike due to high speeds and the complex interactions in dense traffic.
Robotics Signal Processing
Characterizing the limits of human stability during motion: perturbative experiment validates a model-based approach for the Sit-to-Stand task
To address this limitation, the recently proposed Stability Basin (SB) aims to characterize the set of perturbations that will cause an individual to fall under a specific motor control strategy.
LiStereo: Generate Dense Depth Maps from LIDAR and Stereo Imagery
An accurate depth map of the environment is critical to the safe operation of autonomous robots and vehicles.
Stochastic Sampling Simulation for Pedestrian Trajectory Prediction
Our work demonstrates the effectiveness and potential of using simulation as a substitution for human annotation efforts to train high-performing prediction algorithms such as the DNNs.
Robotics
Bridging the Gap Between Safety and Real-Time Performance in Receding-Horizon Trajectory Design for Mobile Robots
At runtime, the FRS is used to map obstacles to the space of parameterized trajectories, which allows RTD to select a safe trajectory at every planning iteration.
Robotics Systems and Control
Sensor Transfer: Learning Optimal Sensor Effect Image Augmentation for Sim-to-Real Domain Adaptation
This domain shift is especially exaggerated between synthetic and real datasets.
DispSegNet: Leveraging Semantics for End-to-End Learning of Disparity Estimation from Stereo Imagery
Initial disparity estimates are refined with an embedding learned from the semantic segmentation branch of the network.
Bio-LSTM: A Biomechanically Inspired Recurrent Neural Network for 3D Pedestrian Pose and Gait Prediction
In applications such as autonomous driving, it is important to understand, infer, and anticipate the intention and future behavior of pedestrians.
PedX: Benchmark Dataset for Metric 3D Pose Estimation of Pedestrians in Complex Urban Intersections
This paper presents a novel dataset titled PedX, a large-scale multimodal collection of pedestrians at complex urban intersections.
Modeling Camera Effects to Improve Visual Learning from Synthetic Data
Recent work has focused on generating synthetic imagery to increase the size and variability of training data for learning visual tasks in urban scenes.
Failing to Learn: Autonomously Identifying Perception Failures for Self-driving Cars
We show that a state-of-the-art detector, tracker, and our classifier trained only on synthetic data can identify valid errors on KITTI tracking dataset with an average precision of 0. 94.
Optimal Control for Nonlinear Hybrid Systems via Convex Relaxations
This paper considers the optimal control for hybrid systems whose trajectories transition between distinct subsystems when state-dependent constraints are satisfied.
Optimization and Control
Driving in the Matrix: Can Virtual Worlds Replace Human-Generated Annotations for Real World Tasks?
Deep learning has rapidly transformed the state of the art algorithms used to address a variety of problems in computer vision and robotics.
