Zusammenfassung
Making Automated Driving (AD) a reality is a challenging task and still subject of intensive research and development. The core component for realizing AD beside the actual vehicle platform and the sensors for perceiving the environment is the AD system, i.e., the software that enables a vehicle to perform distinct dynamic driving tasks in a distinct operational design domain. Briefly speaking, an operational design domain defines environmental and time-of-delay conditions and restrictions, respectively, the presence or absence of certain traffic, and roadway characteristics.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
Literatur
S. Russell and P. Norvig, Artificial Intelligence - A Modern Approach, 2016.
R. C. Arkin, Behavior-Based Robotics, 1998.
R. Bellman, An Introduction to Artificial Intelligence: Can Computers Think?, 1978.
P. H. Winston, Artificial Intelligence, 1992.
E. Rich and K. Knight, Artificial Intelligence, 1991.
D. I. Poole, R. G. Goebel and A. K. Mackworth, Computational Intelligence: A Logical Approach, 1998.
R. S. Sutton and A. G. Barto, Reinforcement Learning: An Introduction, 1998.
S. D. Pendleton, H. Andersen, X. Du, X. Shen, M. Meghjani, Y. H. Eng, D. Rus and M. H. Ang, “Perception, Planning, Control, and Coordination for Autonomous Vehicles,” 2017.
A. Swief and M. El-Habrouk, “A survey of Automotive Driving Assistance Systems technologies,” 2018.
A. Mousavian, D. Anguelov, J. Flynn and J. Kosecka, “3D Bounding Box Estimation Using Deep Learning and Geometry,” 2017.
X. Chen, K. Kundu, Z. Zhang, H. Ma, S. Fidler and R. Urtasun, “Monocular 3D Object Detection for Autonomous Driving,” 2016.
Y. Xiang, W. Choi, Y. Lin and S. Savarese, “Subcategory-aware Convolutional Neural Networks for Object Proposals and Detection,” 2017.
D. Feng, L. Rosenbaum and K. Dietmayer, “Towards Safe Autonomous Driving: Capture Uncertainty in the Deep Neural Network For Lidar 3D Vehicle Detection,” 2018.
B. Yang, W. Luo and R. Urtasun, “PIXOR: Real-time 3D Object Detection from Point Clouds,” 2019.
C. R. Qi, H. Su, K. Mo and L. J. Guibas, “PointNet: Deep Learning on Point Sets for 3D Classification and Segmentation,” 2017.
H. Gao, B. Cheng, J. Wang, K. Li, J. Zhao and D. Li, “Object Classification using CNN-based Fusion of Vision and LIDAS in Autonomous Vehicle Environment,” 2018.
D. Maturana and S. Scherer, “VoxNet: A 3D Convolutional Neural Network for real-time object recognition,” 2015.
C. R. L. W. Qi, C. Wu, H. Su and L. J. Guibas, “Frustum PointNets for 3D Object Detection from RGB-D Data,” 2018.
Z. Wang, W. Zhan and M. Tomizuka, “Fusing Bird View LIDAR Point Cloud and Front View Camera Image for Deep Object Detection,” 2018.
E. Arnold, O. Y. Al-Jarrah, M. Dianati, S. Fallah, D. Oxtoby and A. Mouzakitis, “A Survey on 3D Object Detection Methods for Autonomous Driving Applications,” 2019.
I. A. Tarmizi and A. A. Aziz, “Vehicle Detection Using Convolutional Neural Network for Autonomous Vehicles,” 2018.
G. Melotti, “Multimodal CNN Pedestrian Classification: a Study on Combining LIDAR and Camera Data,” 2018.
J. Schlosser, C. K. Chow and Z. Kira, “Fusing LIDAR and images for pedestrian detection using convolutional neural networks,” 2016.
D. A. Ridel, E. Rehder, M. Lauer, C. Stiller and D. Wolf, “A Literature Review on the Prediction of Pedestrian Behavior in Urban Scenarios,” 2018.
S. Jugade, A. Victorino, V. Berge-Cherfaoui and S. Kanarachos, “Sensor based Prediction of Human Driving Decisions using Feed forward Neural Networks for Intelligent Vehicles,” 2018.
J. &. C. H. Kim, M. Hwangbo, J. Choi, J. Canny and Y. Kwon, “Deep Traffic Light Detection for Self-driving Cars from a Large-scale Dataset,” 2018.
X. Li, J. Li, X. Hu and J. Yang, “Line-CNN: End-to-End Traffic Line Detection With Line Proposal Unit,” 2019.
W. Schwarting, J. Alonso-Mora and D. Rus, “Planning and Decision-Making for Autonomous Vehicles,” 2018.
T. Okuyama, T. Gonsalves and J. Upadhay, “Autonomous Driving System based on Deep Q Learning,” 2018.
T. P. Lillicrap, J. J. P. A. Hunt, N. Heess, T. Erez, Y. Tassa, D. Silver and D. Wierstra, “Continuous control with deep reinforcement learning,” 2016.
Y. Zhang, P. Sun, Y. Yin, L. Lin and X. Wang, “Human-like Autonomous Vehicle Speed Control by Deep Reinforcement Learning with Double QLearning,” 2018.
M. Kaushik, V. Prasad, M. Krishna and B. Ravindran, “Overtaking Maneuvers in Simulated Highway Driving using Deep Reinforcement Learning,” 2018.
P. Wang, C.-Y. Chan and A. de La Fortelle, “A Reinforcement Learning Based Approach for Automated Lane Change Maneuvers,” 2018.
W. Yuan, M. Yang, H. Li, C. Wang and B. Wang, “End-to-end learning for highprecision lane keeping via multi-state model,” 2018.
Y.-K. &. H. C.-Y. Lai, Y.-H. Huang, C.-W. Huang, Y.-X. Kuo and Y.-C. Chung, “Intelligent Vehicle Collision-Avoidance System with Deep Learning,” 2018.
M. Morsali, J. Aslund and E. Frisk, “Trajectory Planning for Autonomous Vehicles in Time Varying Environments Using Support Vector Machines,” 2018.
M. Braun, S. Krebs, F. Flohr and D. M. Gavrila, “The EuroCity Persons Dataset: A Novel Benchmark for Object Detection,” 2018.
D. M. Gavrila, “EuroCity Persons: A Novel Benchmark for Vulnerable Road User Detection (invited talk),” in ECCV, 2018.
C. Liu, B. Zoph, M. Neumann, J. Shlens, W. Hua, L.-J. Li, L. Fei-Fei, Y. A. J. Huang and K. Murphy, “Progressive Neural Architecture Search,” 2018.
F. Hutter, L. Kotthoff and J. Vanschoren, “Automatic Machine Learning: Methods, Systems, Challenges,” 2019.
D. Silver, T. Hubert, J. Schrittwieser, I. Antonoglou, M. Lai, A. Guez, M. Lanctot, L. Sifre, D. Kumaran, T. Graepel, T. Lillicrap, K. Simonyan and D. Hassabis, “Mastering Chess and Shogi by Self-Play with a General Reinforcement Learning Algorithm,” 2017.
A. de la Fortelle, “The perception-decision gap (invited talk),” in ECCV, 2018.
A. Rasouli, I. Kotseruba and J. K. Tsotsos, “Are They Going to Cross? A Benchmark Dataset and Baseline for Pedestrian Crosswalk Behavior,” 2017.
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2020 Springer Fachmedien Wiesbaden GmbH, ein Teil von Springer Nature
About this paper
Cite this paper
Jungmann, A., Lang, C., Pinsker, F., Kallweit, R., Taubenreuther, M., Butenuth, M. (2020). Artificial intelligence for automated driving – quo vadis?. In: Bertram, T. (eds) Automatisiertes Fahren 2019. Proceedings. Springer Vieweg, Wiesbaden. https://doi.org/10.1007/978-3-658-27990-5_11
Download citation
DOI: https://doi.org/10.1007/978-3-658-27990-5_11
Published:
Publisher Name: Springer Vieweg, Wiesbaden
Print ISBN: 978-3-658-27989-9
Online ISBN: 978-3-658-27990-5
eBook Packages: Computer Science and Engineering (German Language)