Search Results for author:
Found 14 papers, 5 papers with code
Enhancing Multimodal Compositional Reasoning of Visual Language Models with Generative Negative Mining
Leveraging these generative hard negative samples, we significantly enhance VLMs' performance in tasks involving multimodal compositional reasoning.
Robust Autonomous Vehicle Pursuit without Expert Steering Labels
In this work, we present a learning method for lateral and longitudinal motion control of an ego-vehicle for vehicle pursuit.
LiDAR View Synthesis for Robust Vehicle Navigation Without Expert Labels
We train a deep learning model, which takes a LiDAR scan as input and predicts the future trajectory as output.
Multi-Vehicle Trajectory Prediction at Intersections using State and Intention Information
This is done by training a neural network which takes the state and intent of the multiple vehicles to predict their future trajectory.
Biologically Inspired Neural Path Finding
The human brain can be considered to be a graphical structure comprising of tens of billions of biological neurons connected by synapses.
Lateral Ego-Vehicle Control without Supervision using Point Clouds
In this paper, a framework for training a more robust and scalable model for lateral vehicle control is proposed.
Self-Supervised Steering Angle Prediction for Vehicle Control Using Visual Odometry
Vision-based learning methods for self-driving cars have primarily used supervised approaches that require a large number of labels for training.
4Seasons: A Cross-Season Dataset for Multi-Weather SLAM in Autonomous Driving
We present a novel dataset covering seasonal and challenging perceptual conditions for autonomous driving.
Towards Generalizing Sensorimotor Control Across Weather Conditions
The ability of deep learning models to generalize well across different scenarios depends primarily on the quality and quantity of annotated data.
GN-Net: The Gauss-Newton Loss for Multi-Weather Relocalization
Direct SLAM methods have shown exceptional performance on odometry tasks.
Towards Self-Supervised High Level Sensor Fusion
In this paper, we present a framework to control a self-driving car by fusing raw information from RGB images and depth maps.
Latent Space Reinforcement Learning for Steering Angle Prediction
The control module trained with reinforcement learning takes the latent vector as input to predict the correct steering angle.
Semantic Label Reduction Techniques for Autonomous Driving
Semantic segmentation maps can be used as input to models for maneuvering the controls of a car.
Modular Vehicle Control for Transferring Semantic Information Between Weather Conditions Using GANs
To this end, we propose to divide the task of vehicle control into two independent modules: a control module which is only trained on one weather condition for which labeled steering data is available, and a perception module which is used as an interface between new weather conditions and the fixed control module.
