Autonomous Driving

By Johann Marius Zöllner, T. Schamm. 2018

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

Citations in the corpus

Driving Policy Transfer via Modularity and Abstraction (2018, background): “Autonomous driving is typically addressed by highly engineered systems that comprise up to a dozen or more subsystems [14, 26, 33, 49].”
End-to-End Driving Via Conditional Imitation Learning (2017, background): “On one side are highly tuned systems that deploy an array of computer vision algorithms to create a model of the environment, which is then used for planning and control [12].”
CARLA: An Open Urban Driving Simulator (2017, background): “This architecture is in line with most existing autonomous driving systems [21, 8].”
CIRL: Controllable Imitative Reinforcement Learning for Vision-based Self-driving (2018, background): “The ﬁrst type is the highly tuned system that assembles a bunch of visual perception algorithms and then uses model-based planning and control [17].”
Multimodal End-to-End Autonomous Driving (2019, background): “defined modules with particular responsibilities; for instance, to provide environmental perception [1], [2], as well as route planning and maneuver control [3], [4].”
TraPHic: Trajectory Prediction in Dense and Heterogeneous Traffic Using Weighted Interactions (2018, background): “ApolloScape [20] is a large-scale dataset of street views that contain scenes with higher complexities, 2D/3D annotations and pose information, lane markings and video frames.”
Deep Multi-Modal Object Detection and Semantic Segmentation for Autonomous Driving: Datasets, Methods, and Challenges (2019, background): “It offers high potential to decrease traffic congestion, improve road safety, and reduce carbon emissions [3].”
Survey on Deep Neural Networks in Speech and Vision Systems (2019, result): “In line with these research efforts, several car manufacturing companies, such as Audi [207] and Tesla [208], are in active competition for developing next-generation self-driving vehicles with the aid of recent developments in neural network based deep learning techniques.”