Autonomous Driving
Autonomous driving research aims to develop vehicles capable of navigating and operating without human intervention, prioritizing safety and efficiency. Current efforts heavily focus on improving perception (using techniques like 3D Gaussian splatting and Bird's-Eye-View representations), prediction (leveraging diffusion models, transformers, and Bayesian games to handle uncertainty), and planning (employing reinforcement learning, large language models, and hierarchical approaches for decision-making). These advancements are crucial for enhancing the reliability and safety of autonomous vehicles, with significant implications for transportation systems and the broader AI community.
Papers
Learning to Drive Using Sparse Imitation Reinforcement Learning
Yuci Han, Alper Yilmaz
Real-Time Trajectory Planning for Autonomous Driving with Gaussian Process and Incremental Refinement
Cheng Jie, Chen Yingbing, Zhang Qingwen, Gan Lu, Liu Ming
Collaborative 3D Object Detection for Automatic Vehicle Systems via Learnable Communications
Junyong Wang, Yuan Zeng, Yi Gong
Image-Based Conditioning for Action Policy Smoothness in Autonomous Miniature Car Racing with Reinforcement Learning
Bo-Jiun Hsu, Hoang-Giang Cao, I Lee, Chih-Yu Kao, Jin-Bo Huang, I-Chen Wu
Leveraging Dynamic Objects for Relative Localization Correction in a Connected Autonomous Vehicle Network
Yunshuang Yuan, Monika Sester
TC-Driver: Trajectory Conditioned Driving for Robust Autonomous Racing -- A Reinforcement Learning Approach
Edoardo Ghignone, Nicolas Baumann, Mike Boss, Michele Magno
Visual Attention-based Self-supervised Absolute Depth Estimation using Geometric Priors in Autonomous Driving
Jie Xiang, Yun Wang, Lifeng An, Haiyang Liu, Zijun Wang, Jian Liu
CARNet: A Dynamic Autoencoder for Learning Latent Dynamics in Autonomous Driving Tasks
Andrey Pak, Hemanth Manjunatha, Dimitar Filev, Panagiotis Tsiotras
Exploring Diversity-based Active Learning for 3D Object Detection in Autonomous Driving
Jinpeng Lin, Zhihao Liang, Shengheng Deng, Lile Cai, Tao Jiang, Tianrui Li, Kui Jia, Xun Xu
Real-time semantic segmentation on FPGAs for autonomous vehicles with hls4ml
Nicolò Ghielmetti, Vladimir Loncar, Maurizio Pierini, Marcel Roed, Sioni Summers, Thea Aarrestad, Christoffer Petersson, Hampus Linander, Jennifer Ngadiuba, Kelvin Lin, Philip Harris
Bridging Sim2Real Gap Using Image Gradients for the Task of End-to-End Autonomous Driving
Unnikrishnan R Nair, Sarthak Sharma, Udit Singh Parihar, Midhun S Menon, Srikanth Vidapanakal
Knowledge Augmented Machine Learning with Applications in Autonomous Driving: A Survey
Julian Wörmann, Daniel Bogdoll, Christian Brunner, Etienne Bührle, Han Chen, Evaristus Fuh Chuo, Kostadin Cvejoski, Ludger van Elst, Philip Gottschall, Stefan Griesche, Christian Hellert, Christian Hesels, Sebastian Houben, Tim Joseph, Niklas Keil, Johann Kelsch, Mert Keser, Hendrik Königshof, Erwin Kraft, Leonie Kreuser, Kevin Krone, Tobias Latka, Denny Mattern, Stefan Matthes, Franz Motzkus, Mohsin Munir, Moritz Nekolla, Adrian Paschke, Stefan Pilar von Pilchau, Maximilian Alexander Pintz, Tianming Qiu, Faraz Qureishi, Syed Tahseen Raza Rizvi, Jörg Reichardt, Laura von Rueden, Alexander Sagel, Diogo Sasdelli, Tobias Scholl, Gerhard Schunk, Gesina Schwalbe, Hao Shen, Youssef Shoeb, Hendrik Stapelbroek, Vera Stehr, Gurucharan Srinivas, Anh Tuan Tran, Abhishek Vivekanandan, Ya Wang, Florian Wasserrab, Tino Werner, Christian Wirth, Stefan Zwicklbauer
KEMP: Keyframe-Based Hierarchical End-to-End Deep Model for Long-Term Trajectory Prediction
Qiujing Lu, Weiqiao Han, Jeffrey Ling, Minfa Wang, Haoyu Chen, Balakrishnan Varadarajan, Paul Covington