Autonomous Vehicle Engineering

Perception Systems and Sensor Fusion

Sensor Hardware Fundamentals

• LiDAR operation: Understanding time-of-flight measurements, point cloud generation, and the impact of atmospheric conditions on beam reflection.
• Radar systems: Mastery of Frequency Modulated Continuous Wave (FMCW) radar for velocity estimation and range detection in poor visibility.
• Camera systems: Processing high-dynamic-range imagery, shutter speed synchronization, and pixel-level depth estimation.

Advanced Sensor Fusion Algorithms

• Kalman Filtering: Implementing Extended Kalman Filters (EKF) and Unscented Kalman Filters (UKF) to combine noisy sensor data into a singular, accurate estimate of state.
• Occupancy Grid Mapping: Converting raw sensor inputs into a probabilistic 2D or 3D grid to distinguish between free space and obstacles.
• Synchronization: Precise timestamping of sensor data streams to ensure data from different hardware aligns temporally for consistent object tracking.

Localization and Mapping

Coordinate Systems and Geometry

• Transformation Matrices: Using rotation matrices and homogeneous coordinates to map sensors from the vehicle frame to the global coordinate frame (UTM).
• IMU Integration: Utilizing Inertial Measurement Units (IMUs) and wheel encoders for dead reckoning to maintain position in GPS-denied environments.

SLAM (Simultaneous Localization and Mapping)

• Feature Extraction: Identifying landmarks such as lane markings, traffic signs, and structural geometry to correct positional drift.
• Scan Matching: Using algorithms like Iterative Closest Point (ICP) to align real-time LiDAR scans with pre-existing high-definition maps.
• Loop Closure: Recognizing previously visited locations to refine map consistency and reduce cumulative trajectory errors.

Decision Making and Path Planning

Behavioral Planning

• Finite State Machines (FSM): Architecting logic for lane keeping, merging, overtaking, and emergency braking based on traffic rule adherence.
• Probabilistic Modeling: Predicting the intentions of surrounding road users using Markov chains and hidden state estimation to anticipate potential collisions.

Motion Planning Algorithms

• Graph-based searching: Implementing A* and D* algorithms for optimal path finding within a navigable graph of the roadway.
• Sampling-based methods: Utilizing Rapidly-exploring Random Trees (RRT*) to navigate complex, non-holonomic constraint environments efficiently.
• Polynomial Trajectory Generation: Calculating smooth, jerk-minimized trajectories that respect vehicle kinematics and passenger comfort constraints.

Vehicle Control Systems

Control Theory Fundamentals

• PID Controllers: Implementing proportional-integral-derivative loops for precise steering angle and throttle management.
• Model Predictive Control (MPC): Formulating optimization problems to predict future vehicle states over a finite horizon and adjusting control inputs to minimize error relative to a reference path.

Vehicle Dynamics

• Bicycle Model: Applying the kinematic and dynamic bicycle models to represent the movement of the vehicle while accounting for tire slip angles and centrifugal forces.
• Actuator Latency: Compensating for hardware response delays to ensure stability during high-speed maneuvers.

Computer Vision and Machine Learning

Object Detection and Classification

• Convolutional Neural Networks (CNNs): Utilizing architectures like YOLO (You Only Look Once) or Faster R-CNN for real-time bounding box detection of pedestrians, vehicles, and cyclists.
• Semantic Segmentation: Pixel-level classification of images to define drivable road surfaces, curbs, and sidewalk boundaries.

Deep Learning Optimization

• Inference Optimization: Implementing techniques like quantization, pruning, and TensorRT optimization to run complex models on embedded hardware within strict millisecond latency constraints.
• Data Augmentation: Improving model robustness by training on synthetic datasets and diverse environmental conditions.