101 citations · 182 across the 14 of their papers we have counts for
21 papers
Lane-Change in Dense Traffic with Model Predictive Control and Neural Networks
Sangjae Bae, David Isele, Alireza Nakhaei +5
This paper presents an online smooth-path lane-change control framework. We focus on dense traffic where inter-vehicle space gaps are narrow, and cooperation with surrounding drive…
Uncovering the Missing Pattern: Unified Framework Towards Trajectory Imputation and Prediction
Yi Xu, Armin Bazarjani, Hyung-gun Chi +2
Trajectory prediction is a crucial undertaking in understanding entity movement or human behavior from observed sequences. However, current methods often assume that the observed s…
DRAMA: Joint Risk Localization and Captioning in Driving
Srikanth Malla, Chiho Choi, Isht Dwivedi +2
Considering the functionality of situational awareness in safety-critical automation systems, the perception of risk in driving scenes and its explainability is of particular impor…
DIDER: Discovering Interpretable Dynamically Evolving Relations
Enna Sachdeva, Chiho Choi
Effective understanding of dynamically evolving multiagent interactions is crucial to capturing the underlying behavior of agents in social systems. It is usually challenging to ob…
Weakly-Supervised Online Action Segmentation in Multi-View Instructional Videos
Reza Ghoddoosian, Isht Dwivedi, Nakul Agarwal +2
This paper addresses a new problem of weakly-supervised online action segmentation in instructional videos. We present a framework to segment streaming videos online at test time u…
Important Object Identification with Semi-Supervised Learning for Autonomous Driving
Jiachen Li, Haiming Gang, Hengbo Ma +2
Accurate identification of important objects in the scene is a prerequisite for safe and high-quality decision making and motion planning of intelligent agents (e.g., autonomous ve…
Domain Knowledge Driven Pseudo Labels for Interpretable Goal-Conditioned Interactive Trajectory Prediction
Lingfeng Sun, Chen Tang, Yaru Niu +5
Motion forecasting in highly interactive scenarios is a challenging problem in autonomous driving. In such scenarios, we need to accurately predict the joint behavior of interactin…
LOKI: Long Term and Key Intentions for Trajectory Prediction
Harshayu Girase, Haiming Gang, Srikanth Malla +4
Recent advances in trajectory prediction have shown that explicit reasoning about agents' intent is important to accurately forecast their motion. However, the current research act…
RAIN: Reinforced Hybrid Attention Inference Network for Motion Forecasting
Jiachen Li, Fan Yang, Hengbo Ma +3
Motion forecasting plays a significant role in various domains (e.g., autonomous driving, human-robot interaction), which aims to predict future motion sequences given a set of his…
Shared Cross-Modal Trajectory Prediction for Autonomous Driving
Chiho Choi, Joon Hee Choi, Jiachen Li +1
Predicting future trajectories of traffic agents in highly interactive environments is an essential and challenging problem for the safe operation of autonomous driving systems. On…
Social-STAGE: Spatio-Temporal Multi-Modal Future Trajectory Forecast
Srikanth Malla, Chiho Choi, Behzad Dariush
This paper considers the problem of multi-modal future trajectory forecast with ranking. Here, multi-modality and ranking refer to the multiple plausible path predictions and the c…
SSP: Single Shot Future Trajectory Prediction
Isht Dwivedi, Srikanth Malla, Behzad Dariush +1
We propose a robust solution to future trajectory forecast, which can be practically applicable to autonomous agents in highly crowded environments. For this, three aspects are par…
Shared Cross-Modal Trajectory Prediction for Autonomous Driving
Chiho Choi, Joon Hee Choi, Srikanth Malla +1
Predicting future trajectories of traffic agents in highly interactive environments is an essential and challenging problem for the safe operation of autonomous driving systems. On…
EvolveGraph: Multi-Agent Trajectory Prediction with Dynamic Relational Reasoning
Jiachen Li, Fan Yang, Masayoshi Tomizuka +1
Multi-agent interacting systems are prevalent in the world, from pure physical systems to complicated social dynamic systems. In many applications, effective understanding of the s…
TITAN: Future Forecast using Action Priors
Srikanth Malla, Behzad Dariush, Chiho Choi
We consider the problem of predicting the future trajectory of scene agents from egocentric views obtained from a moving platform. This problem is important in a variety of domains…
Potential Field: Interpretable and Unified Representation for Trajectory Prediction
Shan Su, Cheng Peng, Jianbo Shi +1
Predicting an agent's future trajectory is a challenging task given the complicated stimuli (environmental/inertial/social) of motion. Prior works learn individual stimulus from di…
Cooperation-Aware Lane Change Maneuver in Dense Traffic based on Model Predictive Control with Recurrent Neural Network
Sangjae Bae, Dhruv Saxena, Alireza Nakhaei +3
This paper presents a real-time lane change control framework of autonomous driving in dense traffic, which exploits cooperative behaviors of other drivers. This paper focuses on h…
NEMO: Future Object Localization Using Noisy Ego Priors
Srikanth Malla, Isht Dwivedi, Behzad Dariush +1
Predicting the future trajectory of agents from visual observations is an important problem for realization of safe and effective navigation of autonomous systems in dynamic enviro…
DROGON: A Trajectory Prediction Model based on Intention-Conditioned Behavior Reasoning
Chiho Choi, Srikanth Malla, Abhishek Patil +1
We propose a Deep RObust Goal-Oriented trajectory prediction Network (DROGON) for accurate vehicle trajectory prediction by considering behavioral intentions of vehicles in traffic…
Looking to Relations for Future Trajectory Forecast
Chiho Choi, Behzad Dariush
Inferring relational behavior between road users as well as road users and their surrounding physical space is an important step toward effective modeling and prediction of navigat…
Egocentric Vision-based Future Vehicle Localization for Intelligent Driving Assistance Systems
Yu Yao, Mingze Xu, Chiho Choi +3
Predicting the future location of vehicles is essential for safety-critical applications such as advanced driver assistance systems (ADAS) and autonomous driving. This paper introd…