A career roadmap for building self-driving vehicles and autonomous transportation infrastructure. From automotive engineering and embedded systems through computer vision, sensor fusion, and path planning to safety standards and fleet management — this path covers the complete stack of autonomous mobility.
12 milestones in this roadmap
Step 1beginner6-8 weeks
Automotive Engineering Fundamentals
Understand vehicle mechanical and electrical systems including powertrain, braking, steering, suspension, and the CAN bus protocol connecting ECUs.
Curriculum
1Internal combustion and electric powertrain architectures
3Electric power steering and steer-by-wire systems
4Vehicle dynamics: tyre slip models (Pacejka Magic Formula)
5
Step 2intermediate8-10 weeks
Embedded Systems & Real-Time Computing
Master embedded programming for automotive applications with RTOS, AUTOSAR, safety-critical coding standards, and hardware platforms like NVIDIA Jetson.
Curriculum
1Real-time operating systems: FreeRTOS, QNX, and POSIX RT
2AUTOSAR Classic and Adaptive platform architecture
3MISRA C/C++ coding guidelines for safety-critical software
4Interrupt handling, priority inversion, and deterministic scheduling
Step 3advanced8-10 weeks
Computer Vision for Autonomous Vehicles
Apply deep learning computer vision to autonomous driving: lane detection, 3D object detection, semantic segmentation, and real-time inference deployment.
Develop deep expertise in the AV sensor suite: LiDAR operating principles, radar signal processing, camera optics, and each modality's strengths and limitations.
Curriculum
1LiDAR: time-of-flight vs FMCW, scanning vs solid-state
3Camera: lens optics, ISP pipeline, HDR, rolling vs global shutter
4Ultrasonic sensors for close-range parking assist
Step 5advanced8-10 weeks
Sensor Fusion & SLAM
Combine multi-sensor data into unified world representations using Kalman filters, factor graphs, and SLAM for robust localisation in GPS-denied areas.
Curriculum
1Extended and Unscented Kalman Filter for multi-sensor state estimation
2Factor graph optimisation (GTSAM) for smoothing and mapping
3Early, mid-level, and late fusion architecture patterns
4Visual-Inertial Odometry (VIO) and visual SLAM (ORB-SLAM3)
Step 6advanced8-10 weeks
Path Planning & Decision Making
Plan vehicle trajectories and make driving decisions using route planning, local trajectory optimisation, behavioural planning, and motion prediction.
2Local trajectory optimisation: spline-based, lattice, and Frenet frame
3Behavioural planning: finite state machines, hierarchical planners
4Motion prediction: social forces, graph neural networks, trajectron++
Step 7advanced6-8 weeks
Vehicle Dynamics & Control Systems
Master control algorithms that translate planned trajectories into vehicle motion using PID, MPC, and Stanley controllers with vehicle dynamics models.
Curriculum
1PID controller tuning for longitudinal speed control
2Model Predictive Control (MPC) formulation and solver selection
3Stanley and pure pursuit controllers for lateral path tracking
4Bicycle model and multi-body vehicle dynamics
Step 8intermediate4-6 weeks
V2X Communication (Vehicle-to-Everything)
Study vehicle-to-everything communication using DSRC and C-V2X standards, message types, cooperative perception, and vehicular network security.
Curriculum
1DSRC (IEEE 802.11p) vs C-V2X (PC5 sidelink) comparison
2Basic Safety Message (BSM) structure and broadcast
3SPaT/MAP messages for traffic signal communication
4Cooperative perception and collective perception messages
Step 9intermediate6-8 weeks
Simulation & Testing (CARLA, LGSVL)
Master simulation environments for safe AV development, scenario-based testing, sensor noise injection, and automated validation pipelines.
Curriculum
1CARLA simulator: maps, traffic, sensors, weather, and scenarios
2OpenSCENARIO and OpenDRIVE standards for test specification
3Sensor simulation: LiDAR ray casting, camera rendering, radar
3Hazard Analysis and Risk Assessment (HARA) methodology
4Technical and hardware safety requirements decomposition
Step 11intermediate3-4 weeks
Regulatory Landscape & Ethics
Navigate the legal and ethical landscape of autonomous vehicles including regulations, liability frameworks, and societal impact on transportation employment.
Curriculum
1Federal Motor Vehicle Safety Standards (FMVSS) for ADS
4Ethical decision frameworks for unavoidable crash scenarios
Step 12intermediate4-6 weeks
Fleet Management & Autonomous Logistics
Deploy autonomous technology at scale with robotaxi fleet management, autonomous trucking, last-mile delivery, and Mobility-as-a-Service business models.
Curriculum
1Robotaxi fleet orchestration and demand-responsive dispatch
2Autonomous trucking: platooning, hub-to-hub, transfer hub design
3Last-mile delivery robots and sidewalk navigation
Master embedded programming for automotive applications with RTOS, AUTOSAR, safety-critical coding standards, and hardware platforms like NVIDIA Jetson.
Curriculum
1Real-time operating systems: FreeRTOS, QNX, and POSIX RT
2AUTOSAR Classic and Adaptive platform architecture
3MISRA C/C++ coding guidelines for safety-critical software
4Interrupt handling, priority inversion, and deterministic scheduling
5Hardware abstraction layers and board support packages
6Boot sequence, secure boot, and OTA update mechanisms
PyTorchTensorRT (NVIDIA)mmDetection3DKITTI / nuScenes / Waymo Open Dataset
👁️
Step 3advanced8-10 weeks
Computer Vision for Autonomous Vehicles
Apply deep learning computer vision to autonomous driving: lane detection, 3D object detection, semantic segmentation, and real-time inference deployment.
Develop deep expertise in the AV sensor suite: LiDAR operating principles, radar signal processing, camera optics, and each modality's strengths and limitations.
Curriculum
1LiDAR: time-of-flight vs FMCW, scanning vs solid-state
Combine multi-sensor data into unified world representations using Kalman filters, factor graphs, and SLAM for robust localisation in GPS-denied areas.
Curriculum
1Extended and Unscented Kalman Filter for multi-sensor state estimation
2Factor graph optimisation (GTSAM) for smoothing and mapping
3Early, mid-level, and late fusion architecture patterns
4Visual-Inertial Odometry (VIO) and visual SLAM (ORB-SLAM3)
5LiDAR SLAM: LOAM, LIO-SAM, KISS-ICP
6HD map localisation and map-based pose refinement
Master control algorithms that translate planned trajectories into vehicle motion using PID, MPC, and Stanley controllers with vehicle dynamics models.
Curriculum
1PID controller tuning for longitudinal speed control
2Model Predictive Control (MPC) formulation and solver selection
3Stanley and pure pursuit controllers for lateral path tracking
4Bicycle model and multi-body vehicle dynamics
5Traction and stability control systems (ESC, TC)
6Control system validation and hardware-in-the-loop testing
6Impact on transportation employment and transition planning
Tools & Platforms
NHTSA ADS framework documentsSAE J3016 (levels of driving automation)UNECE WP.29 (international regulations)Euro NCAP (safety rating for ADAS)
⚖️
Step 11intermediate3-4 weeks
Regulatory Landscape & Ethics
Navigate the legal and ethical landscape of autonomous vehicles including regulations, liability frameworks, and societal impact on transportation employment.
Curriculum
1Federal Motor Vehicle Safety Standards (FMVSS) for ADS
4Ethical decision frameworks for unavoidable crash scenarios
5Public acceptance research and trust calibration
6Impact on transportation employment and transition planning
Tools & Platforms
NHTSA ADS framework documentsSAE J3016 (levels of driving automation)UNECE WP.29 (international regulations)Euro NCAP (safety rating for ADAS)
5
Mobility-as-a-Service (MaaS) platform design and economics
6Fleet maintenance, charging infrastructure, and utilisation optimisation
Tools & Platforms
Fleet management platforms (Ridecell, Bestmile)SUMO / MATSim (fleet simulation)AWS IoT FleetWiseMapbox / HERE HD Maps
🚛
Step 12intermediate4-6 weeks
Fleet Management & Autonomous Logistics
Deploy autonomous technology at scale with robotaxi fleet management, autonomous trucking, last-mile delivery, and Mobility-as-a-Service business models.
Curriculum
1Robotaxi fleet orchestration and demand-responsive dispatch
2Autonomous trucking: platooning, hub-to-hub, transfer hub design
3Last-mile delivery robots and sidewalk navigation