Classical approaches to autonomy tend to perform well in restricted settings. Data-driven approaches to autonomy have the potential for adaptability, and as a result, robustness. However, deploying machine-learning based approaches on actual robot hardware has proven more challenging than expected.
Autonomous driving technology will influence our cities, societies and life in many ways, including the potential reduction of congestion, increased safety, reduced emissions and others.
Ethical, legislative and technical challenges stand in the way of mainstream adoption of this disruptive technology.
The AI-DO provides a productive and accessible environment for technical advancement in the field and promoted the dissemination of subject-specific literacy to a broad audience.