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MURJ News: A novel deep learning-based approach to autonomous vehicles

The Toyota-CSAIL Joint Research Center aims to reduce traffic casualties through the development of autonomous vehicle technologies

By Shinjini Ghosh

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The Self-Driving Vehicles project under the Toyota-CSAIL Joint Research Center, led by Director of CSAIL, Professor Daniela Rus, aims to further develop autonomous vehicle technologies geared toward the reduction of traffic casualties and accidents. It is focused on the development of precise and sophisticated decisionmaking algorithms, as well as systems that can operate reliably without human input, to perceive and safely navigate vehicles’ surroundings.

The Distributed Robotics Laboratory’s project on robust learning for autonomous vehicles aims to fill the gaps left by existing work in perception aspects of the autonomous driving task, which, despite the use of deep learning and end-to-end control, presents reactionary responses and produces output representations unsuitable for decision making or autonomous navigation. The researchers are hence developing a novel deep learning-based approach that accepts a single frame as input and outputs a control probability distribution for an autonomous vehicle. The approach is being tested on both simulations and real autonomous vehicles for a variety of driving conditions.

Credit: Julien Tromeur via Pixabay

One research article published under this project involves variational end-to-end navigation and localization, drawing inspiration from the core competencies of human drivers—being able to drive and navigate according to a map, localize within the environment, and reason when their visual perception does not match what the map says. The model thus learns steering control directly from raw sensing using three cameras and coarse state estimation from the GPS and IMU. This research article was also a best paper award finalist at the IEEE International Conference on Robotics and Automation (ICRA) 2019.

Another paper presents a pipeline for the three-dimensional detection of vehicles by adopting a two-dimensional detection network and fusing it with a three-dimensional print cloud to generate three-dimensional information, alongside the help of a model fitting algorithm and a two-stage convolutional neural network. A variety of other projects, including a CAD tool for designing superintelligent humancomputer groups, a data-driven parallel autonomy system, a safety interlock for self-driving cars, and all-terrain mobility and navigation systems, are racing to make the world of autonomous vehicles more safe, efficient, and convenient.

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