Autoware is the world’s leading open-source software project for autonomous driving. Autoware is built on Robot Operating System (ROS) and enables commercial deployment of autonomous driving in a broad range of vehicles and applications. Supported by the Autoware Foundation, Autoware consists of all the functionality required for autonomous driving (i.e., perception, localization, planning, control) in a modular architecture with crisply defined interfaces and APIs.Supported by the Autoware Foundation, Autoware consists of all the functionality required for autonomous driving (i.e., perception, localization, planning, control) in a modular architecture with crisply defined interfaces and APIs.

Quanser QCar, the feature vehicle of the Self-Driving Car Research Studio, is an open-architecture, scaled vehicle designed for academic research. It is equipped with a wide range of sensors including LIDAR, 360-degree vision, depth sensor, IMU, encoders, as well as user-expandable IO. The vehicle is powered with an NVIDIA® Jetson™ TX2 supercomputer that gives you exceptional speed and power efficiency. Working individually or in a fleet, Quanser QCar is the ideal vehicle for validating your research concepts such as dataset generation, mapping, navigation, machine learning, artificial intelligence, and many more.

PIXKIT comes with the drive-by-wire hardware platform with a set of sensors to achieve the basic autonomous driving functions, like path planning, path following, vector map building, obstacle avoidance, traffic lights recognition etc. The sensor package includes LiDAR, camera, computing unit, navigation system etc.. PIX platforms have rich open CAN interface for users to integrate the upper control layer for further software development and algorithms testing. PIXKIT have been used by universes, R&D institutes and AV companies around the world including the US, the UK, Switzerland, Australia, Japan, South Korea, Singapore, Germany, the Netherlands, Sweden etc.

We put safety first and are committed to providing passengers with a comfortable and diverse experience.”

Coffee is very important to people. Even if the company’s tea room already has a fully automatic coffee machine, it is always hard to refuse the coffee that can be delivered to the door.

“We put safety first and are committed to providing passengers with a comfortable and diverse experience.”

Large retail restaurants are scattered around, and retail restaurant options within walking distance are low. Let the retail

Scenarios:
Semi-closed area urban services such as street cleaning, security, advertising, charging and delivery etc.

Clients:
Delivery, cleaning, greenery enterprises and autonomous driving companies.

This platform is specifically designed for training autonomous driving. It can accommodate various perception equipment such as a 16/32/128-line lidar, 77GHZ mm-radar, ultrasonic radar, integrated navigator, multi-sensor fusion box, and up to 5 cameras. It uses a high-performance computing unit for data processing, decision-making, and auxiliary equipment such as the display. This platform is handy for training projects on sensor principles of intelligent networked vehicles, function testing, parameter adjustment, fault detection, sub-fusion calibration, and hardware testing.

The Drivable Testing Vehicles (DTV) is a platform that enables both autonomous driving and manual driving in parallel. The vehicle is designed to carry out intensive on-road testing, algorithm deployment, and preliminary autonomous driving task. The DTV can provide all-round assistance to vocational education and academic research institutions, assist instructors, students, as well as enterprise R&D engineers to quickly get started with the vehicles-based test, and promote the rapid implementation of the autonomous driving solution at reasonable cost levels.

Advancement in technology and innovative applications has led to the proliferation of unmanned vehicle research. Many application areas have emerged that require not only multiple vehicles but collaboration between multiple vehicle types. The diversity and complexity of these applications present significant challenges to researchers and developers, such as increasing difficulty in setting up and maintaining the evolving and growing drone fleet, as well as the need for standardized validation and verification platform to facilitate development, evaluation, and analysis. Quanser’s new Autonomous Vehicles Research Studio is the ideal solution for academics looking to build a multi-vehicle research program in a short amount of time.