What is this course about?

It provides advanced training on using AI to power autonomous vehicle perception, decision-making, and deployment in smart mobility ecosystems.

Is this course suitable for beginners?

This is a Professional level program. Complete beginners may find the real-time systems and deep learning modules challenging without basic coding knowledge.

Will I receive a certification?

Yes, an e-Certification and e-Marksheet from DSTC will be provided upon successful completion of modules and capstone exercises.

What are the career prospects?

Graduates can pursue roles such as AV Perception Engineer, Robotics Developer, Systems Safety Analyst, or Smart City Consultant.

💻 Software & Environment Setup

Data Science & Analytics

The computational toolchain and environment used throughout AI in Autonomous Vehicles.

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# Recommended conda environment
conda create -n dstc_env python=3.11 -y
conda activate dstc_env
pip install numpy pandas scikit-learn matplotlib seaborn

Python 3.11 + Jupyter
Interactive analysis notebooks

pandas / NumPy
Data wrangling & numerics

scikit-learn
Modelling & evaluation

Plotly / Seaborn
Statistical visualisation

Supported OS: Linux (Ubuntu 22.04+), macOS, or Windows via WSL2.

Hardware: 16 GB RAM minimum; a CUDA GPU helps for deep-learning modules.

Earn government-registered certification in AI in Autonomous Vehicles

e-Certificate and e-Marksheet issued on successful completion.

View full course →

Scholar Registration

For scholars whose department, college or employer pays the fee. We raise a proforma invoice to your institution; you attach the signed processing letter or bank slip.