Search Results for author:
Found 17 papers, 3 papers with code
PBP: Path-based Trajectory Prediction for Autonomous Driving
Goal-based prediction models simplify multimodal prediction by first predicting 2D goal locations of agents and then predicting trajectories conditioned on each goal.
Safe Control Transitions: Machine Vision Based Observable Readiness Index and Data-Driven Takeover Time Prediction
To make safe transitions from autonomous to manual control, a vehicle must have a representation of the awareness of driver state; two metrics which quantify this state are the Observable Readiness Index and Takeover Time.
Salient Sign Detection In Safe Autonomous Driving: AI Which Reasons Over Full Visual Context
Next, we use a custom salience loss function, Salience-Sensitive Focal Loss, to train a Deformable DETR object detection model in order to emphasize stronger performance on salient signs.
On Salience-Sensitive Sign Classification in Autonomous Vehicle Path Planning: Experimental Explorations with a Novel Dataset
Safe path planning in autonomous driving is a complex task due to the interplay of static scene elements and uncertain surrounding agents.
Predicting Take-over Time for Autonomous Driving with Real-World Data: Robust Data Augmentation, Models, and Evaluation
Using the augmented dataset, we develop and train take-over time (TOT) models that operate sequentially on mid and high-level features produced by computer vision algorithms operating on different driver-facing camera views, showing models trained on the augmented dataset to outperform the initial dataset.
Multimodal Trajectory Prediction Conditioned on Lane-Graph Traversals
Accurately predicting the future motion of surrounding vehicles requires reasoning about the inherent uncertainty in driving behavior.
Ranked #6 on
Trajectory Prediction
on nuScenes
Autonomous Vehicles that Alert Humans to Take-Over Controls: Modeling with Real-World Data
With increasing automation in passenger vehicles, the study of safe and smooth occupant-vehicle interaction and control transitions is key.
Trajectory Prediction in Autonomous Driving with a Lane Heading Auxiliary Loss
Predicting a vehicle's trajectory is an essential ability for autonomous vehicles navigating through complex urban traffic scenes.
Trajectory Prediction for Autonomous Driving based on Multi-Head Attention with Joint Agent-Map Representation
The future trajectories of agents can be inferred using two important cues: the locations and past motion of agents, and the static scene structure.
Trajectory Forecasts in Unknown Environments Conditioned on Grid-Based Plans
We address the problem of forecasting pedestrian and vehicle trajectories in unknown environments, conditioned on their past motion and scene structure.
Ranked #11 on
Trajectory Prediction
on Stanford Drone
Scene Compliant Trajectory Forecast with Agent-Centric Spatio-Temporal Grids
Forecasting long-term human motion is a challenging task due to the non-linearity, multi-modality and inherent uncertainty in future trajectories.
Scene Induced Multi-Modal Trajectory Forecasting via Planning
We address multi-modal trajectory forecasting of agents in unknown scenes by formulating it as a planning problem.
Understanding Pedestrian-Vehicle Interactions with Vehicle Mounted Vision: An LSTM Model and Empirical Analysis
In this paper, we present a data-driven approach to implicitly model pedestrians' interactions with vehicles, to better predict pedestrian behavior.
Looking at the Driver/Rider in Autonomous Vehicles to Predict Take-Over Readiness
Continuous estimation the driver's take-over readiness is critical for safe and timely transfer of control during the failure modes of autonomous vehicles.
Multi-Modal Trajectory Prediction of Surrounding Vehicles with Maneuver based LSTMs
To safely and efficiently navigate through complex traffic scenarios, autonomous vehicles need to have the ability to predict the future motion of surrounding vehicles.
Convolutional Social Pooling for Vehicle Trajectory Prediction
Forecasting the motion of surrounding vehicles is a critical ability for an autonomous vehicle deployed in complex traffic.
How would surround vehicles move? A Unified Framework for Maneuver Classification and Motion Prediction
In this paper we propose a unified framework for surround vehicle maneuver classification and motion prediction that exploits multiple cues, namely, the estimated motion of vehicles, an understanding of typical motion patterns of freeway traffic and inter-vehicle interaction.
