S Abrecht, A Hirsch, S Raafatnia… - … on Intelligent Vehicles, 2024 - ieeexplore.ieee.org
… in the field of deeplearning and impressive performance of deep neural networks (DNNs) … demand for their use in automateddriving (AD) systems. The safety of such systems is of …
… In this paper we apply deepreinforcementlearning to the problem of forming long term driving strategies. We note that there are two major challenges that make autonomousdriving …
L Zhang, R Zhang, T Wu, R Weng… - … and learning systems, 2021 - ieeexplore.ieee.org
… In this article, a safe motion planning algorithm for autonomousvehicles is developed by combining the method, which utilizes a neural network to predict collision probability with the …
… the safety of highly automateddriving functions which make use of machinelearning … considerations when applying machinelearning methods for highly automateddriving. Particular …
… safety evaluation, we design 10 driving routes for each safety-critical scenario. Each driving route … We particularly focus on reinforcementlearning-based self-driving methods, since they …
… Testing and validation on these data sets thus also provide a measurement of robustness of detection techniques including the machinelearning algorithms we propose and test in …
G Li, Y Yang, S Li, X Qu, N Lyu, SE Li - Transportation research part C …, 2022 - Elsevier
… To ensure drivingsafety, we proposed a lane change … deepreinforcementlearning to find a risk-aware driving decision strategy with the minimum expected risk for autonomousdriving. …
Z Zhu, Z Hu, W Dai, H Chen, Z Lv - Safety science, 2022 - Elsevier
… deeplearning approaches solve the safety problems in the interaction between autonomous vehicles … and identify safe interactions between autonomousvehicles and pedestrians. …
… We believe that autonomousvehicles will bring many ben… research challenges to the artificial intelligence community. In this … a safe system, we suggested Bayesian deeplearning which …