Search Results for author: Nachiket Deo

Found 17 papers, 3 papers with code

PBP: Path-based Trajectory Prediction for Autonomous Driving

no code implementations7 Sep 2023 Sepideh Afshar, Nachiket Deo, Akshay Bhagat, Titas Chakraborty, Yunming Shao, Balarama Raju Buddharaju, Adwait Deshpande, Henggang Cui

Goal-based prediction models simplify multimodal prediction by first predicting 2D goal locations of agents and then predicting trajectories conditioned on each goal.

Autonomous Driving Inductive Bias +1

Salient Sign Detection In Safe Autonomous Driving: AI Which Reasons Over Full Visual Context

no code implementations14 Jan 2023 Ross Greer, Akshay Gopalkrishnan, Nachiket Deo, Akshay Rangesh, Mohan Trivedi

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.

Autonomous Driving object-detection +2

Safe Control Transitions: Machine Vision Based Observable Readiness Index and Data-Driven Takeover Time Prediction

no code implementations14 Jan 2023 Ross Greer, Nachiket Deo, Akshay Rangesh, Pujitha Gunaratne, Mohan Trivedi

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.

On Salience-Sensitive Sign Classification in Autonomous Vehicle Path Planning: Experimental Explorations with a Novel Dataset

no code implementations2 Dec 2021 Ross Greer, Jason Isa, Nachiket Deo, Akshay Rangesh, Mohan M. Trivedi

Safe path planning in autonomous driving is a complex task due to the interplay of static scene elements and uncertain surrounding agents.

Autonomous Driving

Predicting Take-over Time for Autonomous Driving with Real-World Data: Robust Data Augmentation, Models, and Evaluation

no code implementations27 Jul 2021 Akshay Rangesh, Nachiket Deo, Ross Greer, Pujitha Gunaratne, Mohan M. Trivedi

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.

Autonomous Driving Data Augmentation

Multimodal Trajectory Prediction Conditioned on Lane-Graph Traversals

1 code implementation28 Jun 2021 Nachiket Deo, Eric M. Wolff, Oscar Beijbom

Accurately predicting the future motion of surrounding vehicles requires reasoning about the inherent uncertainty in driving behavior.

motion prediction Trajectory Prediction

Autonomous Vehicles that Alert Humans to Take-Over Controls: Modeling with Real-World Data

no code implementations23 Apr 2021 Akshay Rangesh, Nachiket Deo, Ross Greer, Pujitha Gunaratne, Mohan M. Trivedi

With increasing automation in passenger vehicles, the study of safe and smooth occupant-vehicle interaction and control transitions is key.

Autonomous Vehicles

Trajectory Prediction in Autonomous Driving with a Lane Heading Auxiliary Loss

no code implementations12 Nov 2020 Ross Greer, Nachiket Deo, Mohan Trivedi

Predicting a vehicle's trajectory is an essential ability for autonomous vehicles navigating through complex urban traffic scenes.

Autonomous Driving Trajectory Prediction

Trajectory Prediction for Autonomous Driving based on Multi-Head Attention with Joint Agent-Map Representation

no code implementations6 May 2020 Kaouther Messaoud, Nachiket Deo, Mohan M. Trivedi, Fawzi Nashashibi

The future trajectories of agents can be inferred using two important cues: the locations and past motion of agents, and the static scene structure.

Autonomous Driving Trajectory Prediction

Trajectory Forecasts in Unknown Environments Conditioned on Grid-Based Plans

1 code implementation3 Jan 2020 Nachiket Deo, Mohan M. Trivedi

We address the problem of forecasting pedestrian and vehicle trajectories in unknown environments, conditioned on their past motion and scene structure.

Trajectory Forecasting

Scene Compliant Trajectory Forecast with Agent-Centric Spatio-Temporal Grids

no code implementations16 Sep 2019 Daniela Ridel, Nachiket Deo, Denis Wolf, Mohan Trivedi

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

no code implementations23 May 2019 Nachiket Deo, Mohan M. Trivedi

We address multi-modal trajectory forecasting of agents in unknown scenes by formulating it as a planning problem.

reinforcement-learning Reinforcement Learning (RL) +1

Understanding Pedestrian-Vehicle Interactions with Vehicle Mounted Vision: An LSTM Model and Empirical Analysis

no code implementations14 May 2019 Daniela A. Ridel, Nachiket Deo, Denis Wolf, Mohan M. Trivedi

In this paper, we present a data-driven approach to implicitly model pedestrians' interactions with vehicles, to better predict pedestrian behavior.

Self-Driving Cars

Looking at the Driver/Rider in Autonomous Vehicles to Predict Take-Over Readiness

no code implementations14 Nov 2018 Nachiket Deo, Mohan M. Trivedi

Continuous estimation the driver's take-over readiness is critical for safe and timely transfer of control during the failure modes of autonomous vehicles.

Autonomous Vehicles

Multi-Modal Trajectory Prediction of Surrounding Vehicles with Maneuver based LSTMs

no code implementations15 May 2018 Nachiket Deo, Mohan M. Trivedi

To safely and efficiently navigate through complex traffic scenarios, autonomous vehicles need to have the ability to predict the future motion of surrounding vehicles.

Autonomous Vehicles motion prediction +2

Convolutional Social Pooling for Vehicle Trajectory Prediction

3 code implementations15 May 2018 Nachiket Deo, Mohan M. Trivedi

Forecasting the motion of surrounding vehicles is a critical ability for an autonomous vehicle deployed in complex traffic.

Trajectory Prediction

How would surround vehicles move? A Unified Framework for Maneuver Classification and Motion Prediction

no code implementations19 Jan 2018 Nachiket Deo, Akshay Rangesh, Mohan M. Trivedi

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.

Autonomous Vehicles General Classification +2

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