Search Results - "2020 IEEE Intelligent Vehicles Symposium (IV)"

The inD Dataset: A Drone Dataset of Naturalistic Road User Trajectories at German Intersections by Bock, Julian, Krajewski, Robert, Moers, Tobias, Runde, Steffen, Vater, Lennart, Eckstein, Lutz

“…Automated vehicles rely heavily on data-driven methods, especially for complex urban environments. Large datasets of real world measurement data in the form of…”
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SemanticPOSS: A Point Cloud Dataset with Large Quantity of Dynamic Instances by Pan, Yancheng, Gao, Biao, Mei, Jilin, Geng, Sibo, Li, Chengkun, Zhao, Huijing

“…3D semantic segmentation is one of the key tasks for autonomous driving system. Recently, deep learning models for 3D semantic segmentation task have been…”
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SalsaNet: Fast Road and Vehicle Segmentation in LiDAR Point Clouds for Autonomous Driving by Aksoy, Eren Erdal, Baci, Saimir, Cavdar, Selcuk

“…In this paper, we introduce a deep encoder-decoder network, named SalsaNet, for efficient semantic segmentation of 3D LiDAR point clouds. SalsaNet segments the…”
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Lane Detection in Low-light Conditions Using an Efficient Data Enhancement: Light Conditions Style Transfer by Liu, Tong, Chen, Zhaowei, Yang, Yi, Wu, Zehao, Li, Haowei

“…Nowadays, deep learning techniques are widely used for lane detection, but application in low-light conditions remains a challenge until this day. Although…”
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Cooperative Perception with Deep Reinforcement Learning for Connected Vehicles by Aoki, Shunsuke, Higuchi, Takamasa, Altintas, Onur

“…Sensor-based perception on vehicles are becoming prevalent and important to enhance road safety. Autonomous driving systems use cameras, LiDAR and radar to…”
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Scalable Active Learning for Object Detection by Haussmann, Elmar, Fenzi, Michele, Chitta, Kashyap, Ivanecky, Jan, Xu, Hanson, Roy, Donna, Mittel, Akshita, Koumchatzky, Nicolas, Farabet, Clement, Alvarez, Jose M.

“…Deep Neural Networks trained in a fully supervised fashion are the dominant technology in perception-based autonomous driving systems. While collecting large…”
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Automated Lane Change Strategy using Proximal Policy Optimization-based Deep Reinforcement Learning by Ye, Fei, Cheng, Xuxin, Wang, Pin, Chan, Ching-Yao, Zhang, Jiucai

“…Lane-change maneuvers are commonly executed by drivers to follow a certain routing plan, overtake a slower vehicle, adapt to a merging lane ahead, etc…”
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RSS-Net: Weakly-Supervised Multi-Class Semantic Segmentation with FMCW Radar by Kaul, Prannay, de Martini, Daniele, Gadd, Matthew, Newman, Paul

“…This paper presents an efficient annotation procedure and an application thereof to end-to-end, rich semantic segmentation of the sensed environment using…”
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LIBRE: The Multiple 3D LiDAR Dataset by Carballo, Alexander, Lambert, Jacob, Monrroy, Abraham, Wong, David, Narksri, Patiphon, Kitsukawa, Yuki, Takeuchi, Eijiro, Kato, Shinpei, Takeda, Kazuya

“…In this work, we present LIBRE: LiDAR Benchmarking and Reference, a first-of-its-kind dataset featuring 10 different LiDAR sensors, covering a range of…”
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A Survey on 3D LiDAR Localization for Autonomous Vehicles by Elhousni, Mahdi, Huang, Xinming

“…LiDAR sensors are becoming one of the most essential sensors in achieving full autonomy for self driving cars. LiDARs are able to produce rich, dense and…”
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Fundamental Considerations around Scenario-Based Testing for Automated Driving by Neurohr, Christian, Westhofen, Lukas, Henning, Tabea, de Graaff, Thies, Mohlmann, Eike, Bode, Eckard

“…The homologation of automated vehicles, being safety-critical complex systems, requires sound evidence for their safe operability. Traditionally, verification…”
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DS-PASS: Detail-Sensitive Panoramic Annular Semantic Segmentation through SwaftNet for Surrounding Sensing by Yang, Kailun, Hu, Xinxin, Chen, Hao, Xiang, Kaite, Wang, Kaiwei, Stiefelhagen, Rainer

“…Semantically interpreting the traffic scene is crucial for autonomous transportation and robotics systems. However, state-of-the-art semantic segmentation…”
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CSG: Critical Scenario Generation from Real Traffic Accidents by Xinxin, Zhang, Fei, Li, Xiangbin, Wu

“…Autonomous driving (AD) is getting closer to our life, but the severe traffic accidents of autonomous vehicle (AV) happened in the past several years warn us…”
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Experimental Validation of a Real-Time Optimal Controller for Coordination of CAVs in a Multi-Lane Roundabout by Chalaki, Behdad, Beaver, Logan E., Malikopoulos, Andreas A.

“…Roundabouts in conjunction with other traffic scenarios, e.g., intersections, merging roadways, speed reduction zones, can induce congestion in a…”
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Sensor Fusion of Camera and Cloud Digital Twin Information for Intelligent Vehicles by Liu, Yongkang, Wang, Ziran, Han, Kyungtae, Shou, Zhenyu, Tiwari, Prashant, L. Hansen, John H.

“…With the rapid development of intelligent vehicles and Advanced Driving Assistance Systems (ADAS), a mixed level of human driver engagements is involved in the…”
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Interaction-aware Kalman Neural Networks for Trajectory Prediction by Ju, Ce, Wang, Zheng, Long, Cheng, Zhang, Xiaoyu, Chang, Dong Eui

“…Forecasting the motion of surrounding obstacles (vehicles, bicycles, pedestrians and etc.) benefits the on-road motion planning for intelligent and autonomous…”
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Developments in Modern GNSS and Its Impact on Autonomous Vehicle Architectures by Joubert, Niels, Reid, Tyler G. R., Noble, Fergus

“…This paper surveys a number of recent developments in modern Global Navigation Satellite Systems (GNSS) and investigates the possible impact on autonomous…”
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Identifying the Operational Design Domain for an Automated Driving System through Assessed Risk by Lee, Chung Won, Nayeer, Nasif, Garcia, Danson Evan, Agrawal, Ankur, Liu, Bingbing

“…Assuring the safety of autonomous vehicles is one of the most significant challenges in the automotive industry. Tech companies and automotive manufacturers…”
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Formalization of Interstate Traffic Rules in Temporal Logic by Maierhofer, Sebastian, Rettinger, Anna-Katharina, Mayer, Eva Charlotte, Althoff, Matthias

“…To allow autonomous vehicles to safely participate in traffic and to avoid liability claims for car manufacturers, autonomous vehicles must obey traffic rules…”
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Clustering Traffic Scenarios Using Mental Models as Little as Possible by Hauer, Florian, Gerostathopoulos, Ilias, Schmidt, Tabea, Pretschner, Alexander

“…Test scenario generation for testing automated and autonomous driving systems requires knowledge about the recurring traffic cases, known as scenario types…”
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