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Action Conditioned Response Prediction with Uncertainty for Automated Vehicles.

, , , , , and . ISPACS, page 1-2. IEEE, (2019)

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Coarse-grained Bubble Razor to exploit the potential of two-phase transparent latch designs., , , , and . DATE, page 1-6. European Design and Automation Association, (2014)Deep Q Learning Based High Level Driving Policy Determination., and . Intelligent Vehicles Symposium, page 226-231. IEEE, (2018)Action Conditioned Response Prediction with Uncertainty for Automated Vehicles., , , , , and . ISPACS, page 1-2. IEEE, (2019)A Hierarchical Motion Planning Framework for Autonomous Driving in Structured Highway Environments., , , and . IEEE Access, (2022)Road Surface Classification Using a Deep Ensemble Network with Sensor Feature Selection., , , , , and . Sensors, 18 (12): 4342 (2018)Weighted validation of heteroscedastic regression models for better selection., and . Stat. Anal. Data Min., 15 (1): 57-68 (2022)Semi-automatic ontology construction for visual media web service., , , , and . ICUIMC, page 69-73. ACM, (2008)Local trajectory planning and control for autonomous vehicles using the adaptive potential field., , and . CCTA, page 987-993. IEEE, (2017)Development of handheld optical coherence tomography based on commercial intra-oral scanner shape for extended clinical utility in dentistry., , , , , , and . Int. J. Imaging Syst. Technol., (January 2024)Deep Distributional Reinforcement Learning Based High-Level Driving Policy Determination., , and . IEEE Trans. Intelligent Vehicles, 4 (3): 416-424 (2019)