An Open Approach to Autonomous Vehicles

By Shinpei Kato, Eijiro Takeuchi, Yoshio Ishiguro, Yoshiki Ninomiya, Kazuya Takeda, T. Hamada. 2015

492 citations recorded by the source corpus.

Subfields: Aerospace Engineering, Automotive Engineering, Computer Vision and Pattern Recognition

How researchers see this paper

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

In Aerospace Engineering

In Automotive Engineering

In Computer Vision and Pattern Recognition

Citations in the corpus

A Survey of Autonomous Driving: Common Practices and Emerging Technologies (2019, methodology): “Autoware [110], Apollo [290], Nvidia DriveWorks [291] and openpilot [292] are amongst the most used software-stacks capable of running an ADS platform in real world.”
Autonomous driving based on accurate localization using multilayer LiDAR and dead reckoning (2017, methodology): “, open source software for autonomous driving, also employs NDT and autonomous driving demonstrations using Autoware have been completed successfully in various fields [13].”
Autoware on Board: Enabling Autonomous Vehicles with Embedded Systems (2018, methodology): “To overcome this circumstance, we have developed open-source software, called Autoware [1], [2] which includes a rich set of software packages and libraries required for autonomous vehicles.”
A Survey on Multimodal Large Language Models for Autonomous Driving (2023, background): “Regulatory and service-wise, autonomous driving technology are receiving increasing government acceptance and public acknowledgment, with numerous companies receiving permits to operate autonomous driving vehicles on public roads in designated regions while more vehicles with autonomous driving…”
DeepVCP: An End-to-End Deep Neural Network for Point Cloud Registration (2019, background): “It is a well-known problem and plays an essential role in many applications, such as LiDAR SLAM [50, 8, 19, 27], 3D reconstruction and mapping [38, 10, 45, 9], positioning and localization [48, 20, 42, 25], object pose estimation [43] and so on.”
APEX: Autonomous Vehicle Plan Verification and Execution (2016, methodology): “Figure 2 shows the planner GUI available as part of Autoware [4].”
Artificial intelligence applications in the development of autonomous vehicles: a survey (2020, unknown): “Google: Toyota Prius [50] Y Y Y Y”
Exploring the performance of ROS2 (2016, methodology): “In future work, we will evaluate real-time applications such as an autonomous driving vehicle [18] as case studies using ROS2.”
L3-Net: Towards Learning Based LiDAR Localization for Autonomous Driving (2019, unknown): “[13] provided an open-source platform, named Autoware.”
The Architectural Implications of Autonomous Driving: Constraints and Acceleration (2018, methodology): “Overview of a state-of-the-art autonomous driving system, which is designed based on recent publications [37] and speciications released by industry companies [48, 72].”
Computing Systems for Autonomous Driving: State of the Art and Challenges (2020, background): “According to [20], if the vehicle is self-driving at 40 km per hour in an urban area and wants the control effective every 1 m, then the whole pipeline’s desired response time should be less than 90 ms.”