Autoware on Board: Enabling Autonomous Vehicles with Embedded Systems

Kato, Shigeo; Tokunaga, Shota; Maruyama, Yukie; Maeda, Seiya; Hirabayashi, Manato; Kitsukawa, Yuki; Monrroy, Abraham; Ando, Takehiro; Fujii, Yusuke; Azumi, Takuya

doi:10.1109/iccps.2018.00035

Cited by 445 publications

(198 citation statements)

References 19 publications

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“…We use the camera and the LIDAR for automatic ground truth label generation. The two sensors can be initially calibrated with correspondences [36], [37]. An accurate calibration is key to obtaining the 3D position of surrounding pedestrians and annotating the large number of videos in our dataset.…”

Section: B Camera-lidar Extrinsic Calibrationmentioning

confidence: 99%

Forecasting Time-to-Collision from Monocular Video: Feasibility, Dataset, and Challenges

Manglik

Weng

Ohn-Bar

et al. 2019

2019 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)

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We explore the possibility of using a single monocular camera to forecast the time to collision between a suitcaseshaped robot being pushed by its user and other nearby pedestrians. We develop a purely image-based deep learning approach that directly estimates the time to collision without the need of relying on explicit geometric depth estimates or velocity information to predict future collisions. While previous work has focused on detecting immediate collision in the context of navigating Unmanned Aerial Vehicles, the detection was limited to a binary variable (i.e., collision or no collision). We propose a more fine-grained approach to collision forecasting by predicting the exact time to collision in terms of milliseconds, which is more helpful for collision avoidance in the context of dynamic path planning. To evaluate our method, we have collected a novel dataset of over 13,000 indoor video segments each showing a trajectory of at least one person ending in a close proximity (a near collision) with the camera mounted on a mobile suitcase-shaped platform. Using this dataset, we do extensive experimentation on different temporal windows as input using an exhaustive list of state-of-the-art convolutional neural networks (CNNs). Our results show that our proposed multi-stream CNN is the best model for predicting time to nearcollision. The average prediction error of our time to nearcollision is 0.75 seconds across the test videos. The project webpage can be found at https://aashi7.github.io/ NearCollision.html.

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Section: B Camera-lidar Extrinsic Calibrationmentioning

confidence: 99%

Forecasting Time-to-Collision from Monocular Video: Feasibility, Dataset, and Challenges

Manglik

Weng

Ohn-Bar

et al. 2019

2019 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)

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“…The stack has a very modular structure and brings its custom-built middleware to exchange information. Since the Apollo stack is embedded in a growing open-source community and many companies have joined the Apollo board, we chose to use Apollo over other open-source stacks such as Autoware [8] or the Junior Driving Stack [1].…”

Section: Driving Stackmentioning

confidence: 99%

Bridging the Gap between Open Source Software and Vehicle Hardware for Autonomous Driving

Keßler

Bernhard

Buechel

et al. 2019

2019 IEEE Intelligent Vehicles Symposium (IV)

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Although many research vehicle platforms for autonomous driving have been built in the past, hardware design, source code and lessons learned have not been made available for the next generation of demonstrators. This raises the efforts for the research community to contribute results based on real-world evaluations as engineering knowledge of building and maintaining a research vehicle is lost. In this paper, we deliver an analysis of our approach to transferring an open source driving stack to a research vehicle.We put the hardware and software setup in context to other demonstrators and explain the criteria that led to our chosen hardware and software design. Specifically, we discuss the mapping of the Apollo driving stack to the system layout of our research vehicle, fortuna, including communication with the actuators by a controller running on a real-time hardware platform and the integration of the sensor setup. With our collection of the lessons learned, we encourage a faster setup of such systems by other research groups in the future.

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“…Recent effort has also been focused on open-source selfdriving. Most notable are Autoware [2], [10] and Apollo [3]. These repositories demonstrate modern self-driving systems but rely heavily on the use of a GPU.…”

Section: Related Workmentioning

confidence: 99%

Building a Winning Self-Driving Car in Six Months

Burnett

Schimpe

Samavi

et al. 2019

2019 International Conference on Robotics and Automation (ICRA)

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The SAE AutoDrive Challenge is a three-year competition to develop a Level 4 autonomous vehicle by 2020. The first set of challenges were held in April of 2018 in Yuma, Arizona. Our team (aUToronto/Zeus) placed first. In this paper, we describe our complete system architecture and specialized algorithms that enabled us to win. We show that it is possible to develop a vehicle with basic autonomy features in just six months relying on simple, robust algorithms. We do not make use of a prior map. Instead, we have developed a multisensor visual localization solution. All of our algorithms run in real-time using CPUs only. We also highlight the closed-loop performance of our system in detail in several experiments.

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Autoware on Board: Enabling Autonomous Vehicles with Embedded Systems

Cited by 445 publications

References 19 publications

Forecasting Time-to-Collision from Monocular Video: Feasibility, Dataset, and Challenges

Forecasting Time-to-Collision from Monocular Video: Feasibility, Dataset, and Challenges

Bridging the Gap between Open Source Software and Vehicle Hardware for Autonomous Driving

Building a Winning Self-Driving Car in Six Months

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