1.9k citations · 2.1k across the 16 of their papers we have counts for
19 papers
An Uncertainty-Weighted Decision Transformer for Navigation in Dense, Complex Driving Scenarios
Zhihao Zhang, Chengyang Peng, Minghao Zhu +2
Autonomous driving in dense, dynamic environments requires decision-making systems that can exploit both spatial structure and long-horizon temporal dependencies while remaining ro…
Bootstrapping Reinforcement Learning with Sub-optimal Policies for Autonomous Driving
Zhihao Zhang, Chengyang Peng, Ekim Yurtsever +1
Automated vehicle control using reinforcement learning (RL) has attracted significant attention due to its potential to learn driving policies through environment interaction. Howe…
Extensive Exploration in Complex Traffic Scenarios using Hierarchical Reinforcement Learning
Zhihao Zhang, Ekim Yurtsever, Keith A. Redmill
Developing an automated driving system capable of navigating complex traffic environments remains a formidable challenge. Unlike rule-based or supervised learning-based methods, De…
GraphRelate3D: Context-Dependent 3D Object Detection with Inter-Object Relationship Graphs
Mingyu Liu, Ekim Yurtsever, Marc Brede +6
Accurate and effective 3D object detection is critical for ensuring the driving safety of autonomous vehicles. Recently, state-of-the-art two-stage 3D object detectors have exhibit…
A Survey on Autonomous Driving Datasets: Statistics, Annotation Quality, and a Future Outlook
Mingyu Liu, Ekim Yurtsever, Jonathan Fossaert +5
Autonomous driving has rapidly developed and shown promising performance due to recent advances in hardware and deep learning techniques. High-quality datasets are fundamental for…
3D Understanding of Deformable Linear Objects: Datasets and Transferability Benchmark
Bare Luka Žagar, Tim Hertel, Mingyu Liu +2
Deformable linear objects are vastly represented in our everyday lives. It is often challenging even for humans to visually understand them, as the same object can be entangled so…
Vision Language Models in Autonomous Driving: A Survey and Outlook
Xingcheng Zhou, Mingyu Liu, Ekim Yurtsever +4
The applications of Vision-Language Models (VLMs) in the field of Autonomous Driving (AD) have attracted widespread attention due to their outstanding performance and the ability t…
Using Collision Momentum in Deep Reinforcement Learning Based Adversarial Pedestrian Modeling
Dianwei Chen, Ekim Yurtsever, Keith Redmill +1
Recent research in pedestrian simulation often aims to develop realistic behaviors in various situations, but it is challenging for existing algorithms to generate behaviors that i…
3D Object Detection with a Self-supervised Lidar Scene Flow Backbone
Ekim Yurtsever, Emeç Erçelik, Mingyu Liu +6
State-of-the-art lidar-based 3D object detection methods rely on supervised learning and large labeled datasets. However, annotating lidar data is resource-consuming, and depending…
Formal Certification Methods for Automated Vehicle Safety Assessment
Tong Zhao, Ekim Yurtsever, Joel Paulson +1
Challenges related to automated driving are no longer focused on just the construction of such automated vehicles (AVs), but in assuring the safety of their operation. Recent advan…
Pedestrian Emergence Estimation and Occlusion-Aware Risk Assessment for Urban Autonomous Driving
Mert Koc, Ekim Yurtsever, Keith Redmill +1
Avoiding unseen or partially occluded vulnerable road users (VRUs) is a major challenge for fully autonomous driving in urban scenes. However, occlusion-aware risk assessment syste…
Temp-Frustum Net: 3D Object Detection with Temporal Fusion
Emeç Erçelik, Ekim Yurtsever, Alois Knoll
3D object detection is a core component of automated driving systems. State-of-the-art methods fuse RGB imagery and LiDAR point cloud data frame-by-frame for 3D bounding box regres…
Predicting Pedestrian Crossing Intention with Feature Fusion and Spatio-Temporal Attention
Dongfang Yang, Haolin Zhang, Ekim Yurtsever +2
Predicting vulnerable road user behavior is an essential prerequisite for deploying Automated Driving Systems (ADS) in the real-world. Pedestrian crossing intention should be recog…
Faraway-Frustum: Dealing with Lidar Sparsity for 3D Object Detection using Fusion
Haolin Zhang, Dongfang Yang, Ekim Yurtsever +2
Learned pointcloud representations do not generalize well with an increase in distance to the sensor. For example, at a range greater than 60 meters, the sparsity of lidar pointclo…
A Vision-based Social Distancing and Critical Density Detection System for COVID-19
Dongfang Yang, Ekim Yurtsever, Vishnu Renganathan +2
Social distancing has been proven as an effective measure against the spread of the infectious COronaVIrus Disease 2019 (COVID-19). However, individuals are not used to tracking th…
Photorealism in Driving Simulations: Blending Generative Adversarial Image Synthesis with Rendering
Ekim Yurtsever, Dongfang Yang, Ibrahim Mert Koc +1
Driving simulators play a large role in developing and testing new intelligent vehicle systems. The visual fidelity of the simulation is critical for building vision-based algorith…
Integrating Deep Reinforcement Learning with Model-based Path Planners for Automated Driving
Ekim Yurtsever, Linda Capito, Keith Redmill +1
Automated driving in urban settings is challenging. Human participant behavior is difficult to model, and conventional, rule-based Automated Driving Systems (ADSs) tend to fail whe…
A Survey of Autonomous Driving: Common Practices and Emerging Technologies
Ekim Yurtsever, Jacob Lambert, Alexander Carballo +1
Automated driving systems (ADSs) promise a safe, comfortable and efficient driving experience. However, fatalities involving vehicles equipped with ADSs are on the rise. The full p…
Risky Action Recognition in Lane Change Video Clips using Deep Spatiotemporal Networks with Segmentation Mask Transfer
Ekim Yurtsever, Yongkang Liu, Jacob Lambert +4
Advanced driver assistance and automated driving systems rely on risk estimation modules to predict and avoid dangerous situations. Current methods use expensive sensor setups and…