Overview of Environment Perception for Intelligent Vehicles

By Hao Zhu, Ka-Veng Yuen, Lyudmila S. Mihaylova, Henry Leung. IEEE transactions on intelligent transportation systems (Print), 2017

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A Survey of Deep Learning Applications to Autonomous Vehicle Control (2019, background): “in autonomous vehicle applications, including planning and decision making [27]–[31], perception [32]–[36], as well as mapping and localisation [37]–[39].”
A survey of deep learning techniques for autonomous driving (2019, background): “n, semantic segmentation and localization in autonomous driving, as well as scene understanding using occupancy maps. Surveys dedicated to autonomous vision and environment perception can be found in [28] and [29]. 4.1 Sensing Hardware: Camera vs. LiDAR Debate Deep learning methods are particularly ”
Research Advances and Challenges of Autonomous and Connected Ground Vehicles (2019, methodology): “Most research focus is on the detection of ideogram-based signs while the detection of text-based signs is more time-consuming [111].”
Computer Vision for Autonomous Vehicles: Problems, Datasets and State-of-the-Art (2017, background): “Complementary to our survey, Zhu et al. (2017) provide an overview of environment perception for intelligent vehicles, focusing on lane detection, traffic sign/light recognition as well as vehicle tracking.”