CoCar NextGen: A Highly Flexible and Modular Platform for Comprehensive Autonomous Driving Research

Core Concepts

CoCar NextGen is a highly flexible and modular research platform designed to support a wide range of autonomous driving research scenarios, featuring an extensive multi-modal sensor suite and powerful computing capabilities.

Abstract

The CoCar NextGen is a research vehicle developed by the FZI Research Center for Information Technology to serve as a versatile platform for autonomous driving research. The vehicle is designed with a focus on modularity and flexibility to accommodate a variety of use cases, including:

Recording of real-world sensor data
Closed-loop testing of automated driving software
Communication with connected intelligent infrastructure
Studies on user acceptance and experience
To achieve this, the vehicle is equipped with an extensive sensor suite, including:

6 4D-LiDAR scanners for high-resolution 3D perception
4 mid-range and 2 long-range 360° LiDAR sensors for comprehensive environmental coverage
9 full-HD cameras for semantic information and high-resolution imaging
3 4D radar sensors for robust perception in adverse conditions
The sensor setup is designed to provide 360° surround coverage and minimize blind spots, enabling cross-domain research on multi-modal sensor fusion and perception.
The vehicle also features a powerful computing platform with two Intel Xeon Platinum CPUs, three Nvidia A6000 GPUs, and high-speed storage, allowing it to handle computationally intensive tasks such as machine learning-based driving functions.
To facilitate flexible software development and deployment, the vehicle is equipped with a modular and standardized interface design, including programmable switches, displays, and connectivity options. The power supply system is also designed for extended operation, with a 10 kWh battery and the ability to draw power from the vehicle's hybrid drivetrain or external sources.
Overall, the CoCar NextGen represents a comprehensive and highly capable research platform that can support a wide range of autonomous driving research activities, from sensor data collection to closed-loop testing of advanced driving algorithms.

Stats

The CoCar NextGen is equipped with a powerful computing platform featuring:

Two Intel Xeon Platinum 8352M 32-core CPUs (128 threads total)
768 GB of RAM
Three Nvidia A6000 GPUs with 38.7 TFLOPS and 48 GB VRAM each
Eight 7.68 TB NVMe SSDs in a RAID-0 configuration, providing a total measured disk write bandwidth of 33.4 Gbit/s

Quotes

"Our goal was to create a platform that is able to cover all our research use cases. As opposed to a single use case vehicle, this general approach demands for an extensive hardware setup."
"To be able to deploy arbitrary software, the computing platform should be general purpose rather than dedicated embedded hardware."
"The variety of use cases demand an extensive hardware setup. Therefore, this vehicle must not be understood as a minimal setup to conduct automated driving, rather than a platform for future research."

Key Insights Distilled From

CoCar NextGen: a Multi-Purpose Platform for Connected Autonomous Driving Research

by Marc... at arxiv.org 04-29-2024

https://arxiv.org/pdf/2404.17550.pdf

Deeper Inquiries

How can the modular and flexible design of the CoCar NextGen platform be leveraged to support cross-domain research and the development of novel perception and decision-making algorithms for autonomous driving?

The modular and flexible design of the CoCar NextGen platform plays a crucial role in supporting cross-domain research and the development of innovative perception and decision-making algorithms for autonomous driving in several ways:

Interchangeable Components: The platform's modular nature allows for easy swapping of components, enabling researchers to test different sensors, computing systems, and algorithms without the need for extensive reconfiguration. This interchangeability facilitates cross-domain research by enabling the integration of diverse technologies and methodologies.

Scalability: The flexibility of the platform enables scalability, allowing researchers to add new sensors or computing resources as needed. This scalability is essential for accommodating the increasing complexity of autonomous driving systems and the growing demands of cross-domain research.

Adaptability to Various Scenarios: The platform's flexibility allows researchers to adapt the sensor suite and computing platform to different driving scenarios, such as urban environments, highways, or adverse weather conditions. This adaptability is crucial for testing and refining algorithms across a wide range of real-world situations.

Data-Driven Approaches: The extensive sensor setup of the CoCar NextGen platform, combined with its high flexibility, enables the development of data-driven approaches for perception and decision-making. Researchers can collect diverse and comprehensive data from multiple sensors, facilitating the training and validation of algorithms for autonomous driving.

Cross-Domain Collaboration: The platform's modular design and flexibility also support collaboration between researchers from different domains, such as computer vision, machine learning, and robotics. By providing a common research platform that can be easily customized to different research needs, the CoCar NextGen fosters interdisciplinary collaboration and the exchange of ideas and methodologies.

Overall, the modular and flexible design of the CoCar NextGen platform serves as a versatile and adaptable research tool that empowers researchers to explore new frontiers in autonomous driving technology through cross-domain research and the development of advanced perception and decision-making algorithms.

What are the potential challenges and limitations in maintaining and upgrading the extensive sensor suite and computing platform of the CoCar NextGen over time, and how can these be addressed?

Maintaining and upgrading the extensive sensor suite and computing platform of the CoCar NextGen over time may pose several challenges and limitations, including:

Technological Obsolescence: As technology advances rapidly, components of the sensor suite and computing platform may become obsolete, leading to compatibility issues and reduced performance. Regular upgrades and replacements may be necessary to keep the platform up-to-date.

Integration Complexity: With a large number of sensors and computing resources, integrating new components into the platform can be complex and time-consuming. Ensuring seamless integration and compatibility between old and new components is crucial to maintaining the platform's functionality.

Cost Constraints: Upgrading the sensor suite and computing platform with the latest technologies can be costly. Budget constraints may limit the frequency and extent of upgrades, potentially hindering the platform's ability to stay at the forefront of autonomous driving research.

Software Compatibility: Upgrading hardware components may require corresponding software updates to ensure compatibility and optimal performance. Managing software dependencies and ensuring smooth transitions during upgrades can be challenging.

To address these challenges and limitations, the following strategies can be implemented:

Regular Maintenance: Implement a regular maintenance schedule to monitor the condition of components, identify potential issues early, and perform necessary repairs or replacements to prevent system failures.

Long-Term Planning: Develop a long-term roadmap for upgrades and replacements based on technological advancements and research needs. Prioritize upgrades based on criticality and impact on research outcomes.

Collaboration with Industry Partners: Collaborate with industry partners to stay informed about the latest technologies and trends in autonomous driving. Leverage industry expertise and resources for cost-effective upgrades and access to cutting-edge solutions.

Modular Design: Maintain the platform's modular design to facilitate easy upgrades and replacements. Ensure that new components can be seamlessly integrated into the existing system without major disruptions.

By proactively addressing these challenges and limitations through strategic planning, collaboration, and a focus on modular design, the CoCar NextGen platform can continue to evolve and remain a state-of-the-art research tool for autonomous driving research.

Given the focus on real-world data collection and testing, how can the CoCar NextGen platform contribute to the development of more robust and reliable autonomous driving systems that can handle diverse and unpredictable driving scenarios?

The CoCar NextGen platform's emphasis on real-world data collection and testing offers significant contributions to the development of robust and reliable autonomous driving systems capable of handling diverse and unpredictable driving scenarios:

Data-Driven Algorithm Development: The platform's extensive sensor suite enables the collection of rich and diverse real-world data, which is essential for training and validating perception and decision-making algorithms. By leveraging this data, researchers can develop algorithms that are more robust and reliable in handling various driving scenarios.

Scenario-Based Testing: The platform's flexibility allows for testing in a wide range of scenarios, including urban environments, highways, adverse weather conditions, and complex traffic situations. By exposing autonomous driving systems to diverse scenarios, researchers can identify weaknesses, refine algorithms, and improve system performance under different conditions.

Validation of Safety-Critical Features: Real-world testing on the CoCar NextGen platform provides an opportunity to validate safety-critical features of autonomous driving systems, such as collision avoidance, pedestrian detection, and emergency braking. By subjecting these features to real-world scenarios, researchers can ensure their effectiveness and reliability in preventing accidents.

User Experience Studies: The platform's capability for user experience studies allows researchers to evaluate how passengers interact with autonomous driving systems in real-world conditions. Understanding user behavior and preferences can lead to the development of more user-friendly and trustworthy autonomous systems.

Regulatory Compliance: By conducting real-world testing on public roads, the CoCar NextGen platform can contribute to the validation of autonomous driving systems for regulatory compliance. Demonstrating the safety and reliability of these systems in diverse driving scenarios is essential for gaining regulatory approval for deployment.

Continuous Improvement: The platform's focus on real-world data collection enables continuous improvement of autonomous driving systems through iterative testing, feedback, and refinement. By analyzing data from actual driving scenarios, researchers can identify areas for improvement and implement enhancements to enhance system performance and reliability.

Overall, the CoCar NextGen platform's dedication to real-world data collection and testing plays a vital role in advancing the development of more robust and reliable autonomous driving systems that can effectively navigate diverse and unpredictable driving scenarios with safety and efficiency.