Search Results for author:
Found 19 papers, 5 papers with code
Mesoscale Traffic Forecasting for Real-Time Bottleneck and Shockwave Prediction
Accurate real-time traffic state forecasting plays a pivotal role in traffic control research.
TSGN: Temporal Scene Graph Neural Networks with Projected Vectorized Representation for Multi-Agent Motion Prediction
Meanwhile, we propose a Hierarchical Lane Transformer for capturing interactions between agents and road network, which filters the surrounding road network and only keeps the most probable lane segments which could have an impact on the future behavior of the target agent.
Uncertainty estimation for Cross-dataset performance in Trajectory prediction
While a lot of work has been carried on developing trajectory prediction methods, and various datasets have been proposed for benchmarking this task, little study has been done so far on the generalizability and the transferability of these methods across dataset.
Assessing Cross-dataset Generalization of Pedestrian Crossing Predictors
Pedestrian crossing prediction has been a topic of active research, resulting in many new algorithmic solutions.
STIR$^2$: Reward Relabelling for combined Reinforcement and Imitation Learning on sparse-reward tasks
Our method is based on a reward bonus given to demonstrations and successful episodes (via relabeling), encouraging expert imitation and self-imitation.
GRI: General Reinforced Imitation and its Application to Vision-Based Autonomous Driving
Deep reinforcement learning (DRL) has been demonstrated to be effective for several complex decision-making applications such as autonomous driving and robotics.
Ranked #4 on
CARLA MAP Leaderboard
on CARLA
Learning from demonstrations with SACR2: Soft Actor-Critic with Reward Relabeling
We also present a new method for sparse-reward tasks, based on a reward bonus given to demonstrations and successful episodes.
THOMAS: Trajectory Heatmap Output with learned Multi-Agent Sampling
In this paper, we propose THOMAS, a joint multi-agent trajectory prediction framework allowing for an efficient and consistent prediction of multi-agent multi-modal trajectories.
Ranked #6 on
Trajectory Prediction
on nuScenes
GOHOME: Graph-Oriented Heatmap Output for future Motion Estimation
In this paper, we propose GOHOME, a method leveraging graph representations of the High Definition Map and sparse projections to generate a heatmap output representing the future position probability distribution for a given agent in a traffic scene.
Ranked #1 on
Trajectory Prediction
on INTERACTION Dataset - Validation
(minFDE6 metric)
TrouSPI-Net: Spatio-temporal attention on parallel atrous convolutions and U-GRUs for skeletal pedestrian crossing prediction
In this paper, we address pedestrian crossing prediction in urban traffic environments by linking the dynamics of a pedestrian's skeleton to a binary crossing intention.
Asymmetrical Bi-RNN for pedestrian trajectory encoding
Pedestrian motion behavior involves a combination of individual goals and social interactions with other agents.
Ranked #2 on
Trajectory Prediction
on TrajNet++
HOME: Heatmap Output for future Motion Estimation
In this paper, we propose HOME, a framework tackling the motion forecasting problem with an image output representing the probability distribution of the agent's future location.
Ranked #32 on
Motion Forecasting
on Argoverse CVPR 2020
End-to-End Model-Free Reinforcement Learning for Urban Driving using Implicit Affordances
To our knowledge we are the first to present a successful RL agent handling such a complex task especially regarding the traffic light detection.
Ranked #13 on
Autonomous Driving
on CARLA Leaderboard
Is Deep Reinforcement Learning Really Superhuman on Atari? Leveling the playing field
In the Arcade Learning Environment (ALE), small changes in environment parameters such as stochasticity or the maximum allowed play time can lead to very different performance.
Multiview Based 3D Scene Understanding On Partial Point Sets
Deep learning within the context of point clouds has gained much research interest in recent years mostly due to the promising results that have been achieved on a number of challenging benchmarks, such as 3D shape recognition and scene semantic segmentation.
Coupled Longitudinal and Lateral Control of a Vehicle using Deep Learning
This paper explores the capability of deep neural networks to capture key characteristics of vehicle dynamics, and their ability to perform coupled longitudinal and lateral control of a vehicle.
End to End Vehicle Lateral Control Using a Single Fisheye Camera
Experiments are conducted on a custom dataset corresponding to more than 10000 km and 200 hours of open road driving.
Deep Learning for Hand Gesture Recognition on Skeletal Data
In this paper, we introduce a new 3D hand gesture recognition approach based on a deep learning model.
Ranked #5 on
Hand Gesture Recognition
on DHG-14
Monocular Urban Localization using Street View
This paper presents a metric global localization in the urban environment only with a monocular camera and the Google Street View database.
