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Autonomous Systems and Self- Driving Cars

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Autonomous Systems and Self- Driving Cars

Last Updated : July 29, 2025
F4courses
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About Course

This intensive course provides an in-depth exploration of the theoretical foundations and practical engineering
challenges involved in designing and deploying
autonomous systems, with a strong focus on self-
driving vehicles. Students will gain hands-on experience
with key algorithms and software frameworks,
empowering them to build systems that can perceive,
understand, make intelligent decisions, and safely
navigate dynamic environments independently.

What Will You Learn?

  • Analyze and design the fundamental architectural components of diverse autonomous systems, including understanding their levels of autonomy (SAE J3016).
  • Implement advanced computer vision and perception algorithms, such as object detection (e.g., YOLO, Faster R-CNN) and semantic segmentation, for robust environmental understanding.
  • Master multisensor fusion techniques, including Kalman and particle filters, to seamlessly integrate data from various sources like cameras, LiDAR, RADAR, and ultrasonic sensors.
  • Develop and optimize sophisticated path planning (e.g., A*, RRT) and decision-making algorithms (e.g., finite state machines, reinforcement learning) for
  • complex, real-world navigation
  • Design and implement precise control systems (e.g., PID, Model Predictive Control) to ensure stable and safe autonomous vehicle operation and accurate
  • trajectory following.
  • Critically evaluate and address the multifaceted challenges of safety validation, reliability, cybersecurity, and the intricate ethical and legal
  • implications inherent in autonomous system deployment.

Course Content

Introduction to Autonomous Systems Architectures
Begin with a deep dive into the hierarchical and modular architectures of autonomous systems, including SAE International's levels of driving automation (J3016). Review the historical progression from ADAS to fully autonomous driving, identifying core challenges in perception, localization, planning, and control. Explore diverse application domains, including drones, robotics, and industrial automation.

Advanced Perception and Sensor Technologies
Examine the principles and applications of essential sensor technologies, including high-resolution cameras, 3D LiDAR, automotive RADAR, and ultrasonic sensors. Implement state-of-the-art computer vision techniques for real-time object detection (e.g., YOLOv7, DETR), object classification, tracking (e.g., SORT, DeepSORT), and instance segmentation. Learn about robust environmental mapping using occupancy grids, point clouds, and HD maps, alongside precise localization algorithms such as GPS-IMU fusion and visual odometry.

Multi-Sensor Fusion and State Estimation
Master methodologies for combining heterogeneous sensor data to achieve a comprehensive and robust understanding of the environment. Implement advanced state estimation techniques including Extended Kalman Filters (EKF), Unscented Kalman Filters (UKF), and particle filters for tracking dynamic objects and estimating vehicle pose. Dive into Simultaneous Localization and Mapping (SLAM) algorithms (e.g., EKF-SLAM, Graph SLAM) to build consistent maps while simultaneously localizing the vehicle within them, addressing challenges like sensor noise and data association.

Decision Making, Behavioral, and Motion Planning
Study algorithms for high-level behavior planning, including finite state machines, decision trees, and utility-based models for complex traffic scenarios. Implement global route planning algorithms (e.g., Dijkstra's, A*, Rapidly-exploring Random Tree - RRT) and local motion planning algorithms (e.g., Dynamic Window Approach - DWA, Model Predictive Path Integral - MPPI) for smooth and collision-free trajectories. Explore reinforcement learning (e.g., Q- learning, Deep Q-Networks, Policy Gradients) for autonomous decision-making under uncertainty and adapting to unforeseen circumstances.

Longitudinal and Lateral Control Systems
Delve into vehicle dynamics modeling, including kinematic and dynamic bicycle models, crucial for accurate control. Implement and tune classical control strategies such as PID controllers for speed and steering. Explore advanced control techniques like Model Predictive Control (MPC) and Linear Quadratic Regulator (LQR) for trajectory following and robust performance in varying conditions. Focus on achieving stability, precise trajectory tracking, and incorporating fail-safe mechanisms for critical operations.

Safety, Ethics, and Regulatory Frameworks
Investigate rigorous safety validation and verification methods for autonomous systems, including hardware-in-the-loop (HIL) and software-in-the- loop (SIL) testing. Understand fault detection, diagnosis, and redundancy strategies to enhance system reliability. Address profound ethical considerations in autonomous system deployment, such as the "trolley problem" and responsibility in accidents. Analyze current international and national regulatory frameworks (e.g., ISO 26262, UNECE regulations) and discuss future policy directions, along with the broader societal impacts of widespread autonomous technology adoption.

Capstone Project
Students will design and implement a significant functional component or a full subsystem for a simulated autonomous vehicle, utilizing industry-standard simulation tools. Potential projects include developing a robust perception pipeline capable of detecting and tracking multiple object classes in challenging weather conditions, creating an advanced path planning system that optimizes for comfort and efficiency in dense urban environments, or engineering a precise control algorithm that ensures smooth lane changes and emergency braking. The project culminates in a comprehensive technical report detailing methodology, implementation, rigorous testing results using quantitative metrics, and a comparative analysis against existing solutions.

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