Deep learning algorithm for autonomous driving using GoogLeNet

By Mohammed Al-Qizwini, Iman Barjasteh, Hothaifa Al-Qassab, Hayder M. S. Radha. 2017

239 citations recorded by the source corpus.

Subfields: Automotive Engineering, Computer Vision and Pattern Recognition

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Citations in the corpus

VEGA: Towards an End-to-End Configurable AutoML Pipeline (2020, background): “…intelligence in various tasks, such as computer vision (He et al., 2016; Ren et al., 2015), natural language processing (NLP) (Yin et al., 2017; Devlin et al., 2018), autonomous driving (Al-Qizwini et al., 2017; Kuutti et al., 2020) and recommender systems (Guo et al., 2017; Liu et al., 2020a).”
SS-IL: Separated Softmax for Incremental Learning (2020, methodology): “Following the recent success in many different applications [14, 3, 7], the DNN-based incremental learning methods have been also actively pursued in recent years.”
Autonomous Cars: Research Results, Issues, and Future Challenges (2019, methodology): “In the other category called, behavior reflex mode, the neural network is used to train the system based on human behavior which is closely observed and learnt to take decisions for autonomous driving [61], [62].”
A Simple Class Decision Balancing for Incremental Learning (2020, methodology): “Following the recent success of DNN in many different applications [14,3,5], the DNN-based incremental learning methods have been also actively pursued in recent years.”
Conditional Vehicle Trajectories Prediction in CARLA Urban Environment (2019, background): “Direct perception is a possible solution: instead of commands, the network predicts hand-picked parameters relevant to the driving (distance to the lines, to other vehicles), which are then fed to an independent controller [6, 1, 30].”
Conditional Affordance Learning for Driving in Urban Environments (2018, methodology): “ly proposed by Gibson [22] in the ﬁeld of psychology and has been applied to the autonomous driving task by Chen et al. [2], demonstrating strong performance in a racing simulation. Al-Qizwini et al. [23] improve the original approach of Chen et al. [2] by analyzing different CNNs for the mapping fr”
A Survey of End-to-End Driving: Architectures and Training Methods (2020, background): “Direct perception approaches [84], [110], [111] predict human-understandable affordances that can then be used by hard-coded controllers to make detailed driving decisions.”