1. Prerequisites

To understand advanced perception techniques like LiDAR, Liquid Neural Networks, and Vision Transformers, the following foundational concepts are required:

1.1 Mathematics & Linear Algebra

• Vectors & Matrices: Essential for transformations and feature extractions.
• Eigenvalues & Eigenvectors: Used in Principal Component Analysis (PCA) for feature reduction.
• Probability & Statistics: Fundamental for uncertainty modeling and decision-making.

1.2 Machine Learning & Deep Learning

• Neural Networks: Understanding basic architectures like MLPs, CNNs, and RNNs.
• Backpropagation: Essential for training deep networks.
• Attention Mechanisms: Required for Vision Transformers (ViTs).

1.3 Computer Vision & Image Processing

• Feature Extraction: SIFT, ORB, and deep feature maps.
• Convolutional Neural Networks (CNNs): Foundation for ViTs.
• Object Detection: YOLO, SSD, Faster R-CNN.

1.4 Robotics & Autonomous Systems

• Sensor Fusion: Combining LiDAR, cameras, and IMUs.
• SLAM (Simultaneous Localization and Mapping): Used in autonomous navigation.

1.5 Data Structures & Algorithms

• Graph Theory: Used in point cloud segmentation and path planning.
• Optimization Techniques: SGD, Adam, and reinforcement learning.

2. Core Concepts of Advanced Perception

2.1 LiDAR (Light Detection and Ranging)

LiDAR is a remote sensing technology that uses laser pulses to measure distances and create 3D maps.

• Working Principle: Emits laser pulses, measures time taken for return, and constructs a point cloud.
• Types: Mechanical LiDAR, Solid-state LiDAR.
• Applications: Autonomous vehicles, robotics, mapping, and environmental monitoring.

2.2 Liquid Neural Networks (LNNs)

Liquid Neural Networks are biologically inspired neural networks where neuron dynamics continuously evolve over time.

• Key Feature: Adaptive, continuous-time behavior making them robust to changing inputs.
• Difference from Standard Neural Networks: Uses differential equations to model neurons instead of static weights.
• Applications: Time-series predictions, autonomous robotics, sensor fusion.

2.3 Vision Transformers (ViTs)

Vision Transformers apply Transformer architectures to images instead of sequential text.

• Key Concept: Images are divided into patches, which are processed like tokens in NLP.
• Advantage: Captures long-range dependencies better than CNNs.
• Applications: Object recognition, medical imaging, anomaly detection.

3. Why Do These Algorithms Exist?

3.1 Autonomous Vehicles

• LiDAR: Used for 3D environment perception and obstacle detection.
• Vision Transformers: Used for object recognition and lane detection.
• Liquid Neural Networks: Enables fast adaptation to changing road conditions.

3.2 Robotics & Industrial Automation

• LiDAR: Used in SLAM for robot navigation.
• Vision Transformers: Helps in robotic vision for object manipulation.
• Liquid Neural Networks: Enables flexible decision-making in dynamic environments.

3.3 Medical Imaging & Healthcare

• Vision Transformers: Used in MRI and X-ray analysis.
• Liquid Neural Networks: Used for ECG and EEG signal analysis.

4. When Should You Use It?

4.1 When High-Precision Depth Perception is Required

Use LiDAR when depth estimation and 3D mapping are necessary, such as in autonomous driving.

4.2 When Handling Complex Time-Series Data

Use Liquid Neural Networks for adaptive learning in unpredictable environments, such as financial markets and autonomous systems.

4.3 When Handling Large-Scale Image Processing Tasks

Use Vision Transformers when CNNs struggle with long-range dependencies in images, such as high-resolution medical scans and satellite imagery.

5. How Do They Compare to Alternatives?

5.1 LiDAR vs. Cameras vs. Radar

5.2 Liquid Neural Networks vs. Traditional Neural Networks

5.3 Vision Transformers vs. Convolutional Neural Networks

6. Basic Implementation

6.1 LiDAR Point Cloud Processing (Python + Open3D)

The following Python implementation reads a LiDAR point cloud and visualizes it using Open3D.

import open3d as o3d
import numpy as np

# Load a sample point cloud file
point_cloud = o3d.io.read_point_cloud("sample.pcd")

# Visualize the point cloud
o3d.visualization.draw_geometries([point_cloud])

Dry Run: Given a sample point cloud file sample.pcd:

• The function loads the point cloud from the file.
• The visualization module renders the 3D point cloud.

6.2 Liquid Neural Network for Time-Series Prediction

Below is a basic PyTorch implementation of a Liquid Neural Network.

import torch
import torch.nn as nn
import torch.optim as optim

class LiquidNeuralNetwork(nn.Module):
    def __init__(self, input_size, hidden_size, output_size):
        super(LiquidNeuralNetwork, self).__init__()
        self.hidden = nn.Linear(input_size, hidden_size)
        self.output = nn.Linear(hidden_size, output_size)
    
    def forward(self, x):
        x = torch.tanh(self.hidden(x))  # Non-linear dynamics
        return self.output(x)

# Sample Data
input_tensor = torch.tensor([[0.5]], dtype=torch.float32)
model = LiquidNeuralNetwork(1, 5, 1)
output = model(input_tensor)

print(output)  # Output prediction

Dry Run: Given an input of 0.5:

• The input passes through a hidden layer with 5 neurons.
• Tanh activation applies a non-linearity.
• The final output layer predicts the result.

6.3 Vision Transformer (ViT) for Image Classification

A basic Vision Transformer (ViT) model using Hugging Face Transformers.

from transformers import ViTForImageClassification, ViTFeatureExtractor
from PIL import Image
import torch

# Load a pre-trained Vision Transformer model
model = ViTForImageClassification.from_pretrained("google/vit-base-patch16-224")
feature_extractor = ViTFeatureExtractor.from_pretrained("google/vit-base-patch16-224")

# Load and preprocess an image
image = Image.open("sample_image.jpg").convert("RGB")
inputs = feature_extractor(images=image, return_tensors="pt")

# Perform inference
with torch.no_grad():
    outputs = model(**inputs)

# Print the predicted label
predicted_class = outputs.logits.argmax(-1).item()
print(f"Predicted class: {predicted_class}")

Dry Run: Given an image sample_image.jpg:

• The image is processed using a Vision Transformer feature extractor.
• The pre-trained ViT model computes the class logits.
• The highest probability class is printed.

8. Time & Space Complexity Analysis

8.1 LiDAR Point Cloud Processing Complexity

• Worst-case Time Complexity: \(O(N \log N)\) (For sorting or spatial indexing, e.g., KD-Tree, Octree)
• Best-case Time Complexity: \(O(N)\) (Linear traversal for basic processing)
• Average-case Time Complexity: \(O(N \log N)\) (Common in nearest neighbor searches, clustering)

Space Complexity

• Raw Point Cloud Storage: \(O(N)\), where \(N\) is the number of points.
• Memory Usage: Depends on resolution; a high-resolution LiDAR scan generates millions of points.
• Optimized Storage (KD-Tree, Octree): \(O(N)\) but requires additional indexing overhead.

8.2 Liquid Neural Networks Complexity

• Worst-case Time Complexity: \(O(N^2)\) (Fully connected recurrent updates for each timestep)
• Best-case Time Complexity: \(O(N)\) (If sparsity and optimizations are applied)
• Average-case Time Complexity: \(O(N^2)\) (For backpropagation through time, BPTT)

Space Complexity

• Weights Storage: \(O(N^2)\), where \(N\) is the number of neurons.
• Activation Storage: \(O(N)\) per timestep.
• Optimization Techniques: Sparse connectivity reduces overhead.

8.3 Vision Transformers Complexity

• Worst-case Time Complexity: \(O(N^2 \cdot D)\), where \(N\) is the number of tokens and \(D\) is embedding dimension.
• Best-case Time Complexity: \(O(N \cdot D)\) (Linear attention mechanisms can improve efficiency).
• Average-case Time Complexity: \(O(N^2 \cdot D)\) (For standard self-attention computation).

Space Complexity

• Token Representation: \(O(ND)\).
• Attention Matrix Storage: \(O(N^2)\) (Can be reduced with sparse attention).
• Layer-wise Storage: \(O(L \cdot N \cdot D)\), where \(L\) is the number of transformer layers.

9. How Space Consumption Changes with Input Size

9.1 LiDAR Space Growth

• Each additional point in the scan increases storage by \(O(1)\).
• Spatial structures like KD-Trees add an indexing overhead of \(O(N)\).
• Compression techniques reduce space but introduce processing overhead.

9.2 Liquid Neural Networks Space Growth

• Increasing hidden neurons results in quadratic growth in space \(O(N^2)\).
• More timesteps require storing additional past states, increasing memory usage.
• Pruning and weight sharing can reduce this significantly.

9.3 Vision Transformers Space Growth

• Increasing image resolution leads to more tokens (\(O(N)\) complexity per layer).
• Higher layers require more attention storage (\(O(N^2)\)).
• Distillation and quantization techniques reduce space without much performance loss.

10. Trade-offs in Advanced Perception

10.1 LiDAR vs. Camera vs. Radar

10.2 Liquid Neural Networks vs. Standard RNNs

10.3 Vision Transformers vs. Convolutional Neural Networks

11. Optimizations & Variants

11.1 LiDAR Optimizations

Common Optimizations

• Point Cloud Downsampling: Reduces data size while preserving key features. Methods include:
  • Voxel Grid Filtering
  • Random Sampling
• Noise Reduction: Uses statistical outlier removal and median filtering.
• Compression Techniques: Octree-based compression reduces memory consumption.
• Efficient Search Structures: KD-Trees improve nearest neighbor searches from \(O(N^2)\) to \(O(N \log N)\).

Variants

• Solid-State LiDAR: Smaller, cheaper, and more robust than mechanical LiDAR.
• Flash LiDAR: Captures an entire scene in a single pulse, unlike traditional scanning.

11.2 Liquid Neural Networks Optimizations

Common Optimizations

• Sparse Connectivity: Reduces computation from \(O(N^2)\) to \(O(N \log N)\).
• Neural Pruning: Removes redundant neurons to enhance efficiency.
• Adaptive Time-Steps: Instead of fixed updates, time-step sizes change dynamically.

Variants

• Recurrent Liquid Neural Networks: Optimized for time-series data with better memory retention.
• Hybrid Liquid-CNN Models: Combine liquid neurons with CNNs for improved spatial-temporal processing.

11.3 Vision Transformers (ViTs) Optimizations

Common Optimizations

• Linear Attention Mechanisms: Reduces self-attention complexity from \(O(N^2 D)\) to \(O(ND)\).
• Patch Embedding Reduction: Uses larger patches to decrease token count.
• Distilled ViTs: Train smaller models using knowledge distillation.

Variants

• Data-Efficient ViTs: Work well with limited labeled data.
• Hybrid ViTs: Combine CNN feature extraction with Transformer architecture.

12. Iterative vs. Recursive Implementations

12.1 LiDAR Point Cloud Processing

Iterative Implementation (Efficient)

import open3d as o3d

# Load and process LiDAR data iteratively
def process_lidar(file):
    point_cloud = o3d.io.read_point_cloud(file)
    downsampled = point_cloud.voxel_down_sample(voxel_size=0.05)  # Iterative downsampling
    return downsampled

processed = process_lidar("sample.pcd")
o3d.visualization.draw_geometries([processed])

Recursive Implementation (Inefficient for Large Data)

def recursive_downsample(point_cloud, depth):
    if depth == 0:
        return point_cloud
    return recursive_downsample(point_cloud.voxel_down_sample(0.05), depth - 1)

point_cloud = o3d.io.read_point_cloud("sample.pcd")
processed = recursive_downsample(point_cloud, 3)
o3d.visualization.draw_geometries([processed])

Efficiency Comparison

• Iterative: \(O(N)\) complexity, efficient memory usage.
• Recursive: Adds function call overhead, risk of stack overflow.

12.2 Liquid Neural Networks

Iterative Implementation

import torch
import torch.nn as nn

class LiquidNN(nn.Module):
    def __init__(self, input_size, hidden_size, output_size):
        super(LiquidNN, self).__init__()
        self.hidden = nn.Linear(input_size, hidden_size)
        self.output = nn.Linear(hidden_size, output_size)

    def forward(self, x):
        for _ in range(5):  # Iterative updates
            x = torch.tanh(self.hidden(x))
        return self.output(x)

model = LiquidNN(1, 5, 1)
output = model(torch.tensor([[0.5]]))
print(output)

Recursive Implementation

def recursive_forward(model, x, depth):
    if depth == 0:
        return model.output(x)
    x = torch.tanh(model.hidden(x))
    return recursive_forward(model, x, depth - 1)

output = recursive_forward(model, torch.tensor([[0.5]]), 5)
print(output)

Efficiency Comparison

• Iterative: Efficient, direct weight updates.
• Recursive: High function call overhead, stack growth.

12.3 Vision Transformers

Iterative Implementation (Efficient)

from transformers import ViTForImageClassification, ViTFeatureExtractor
from PIL import Image
import torch

model = ViTForImageClassification.from_pretrained("google/vit-base-patch16-224")
feature_extractor = ViTFeatureExtractor.from_pretrained("google/vit-base-patch16-224")

image = Image.open("sample_image.jpg").convert("RGB")
inputs = feature_extractor(images=image, return_tensors="pt")

with torch.no_grad():
    for _ in range(3):  # Iterative forward passes
        outputs = model(**inputs)

print(outputs.logits.argmax(-1).item())

Recursive Implementation (Inefficient)

def recursive_forward(model, inputs, depth):
    if depth == 0:
        return model(**inputs)
    return recursive_forward(model, inputs, depth - 1)

output = recursive_forward(model, inputs, 3)
print(output.logits.argmax(-1).item())

Efficiency Comparison

• Iterative: Memory-efficient, optimized for GPU computation.
• Recursive: Unnecessary stack usage, no parallelism benefits.

13. Edge Cases & Failure Handling

13.1 Common Pitfalls and Edge Cases

LiDAR (Light Detection and Ranging)

• High Noise in Data: Environmental conditions (rain, fog, snow) introduce noise.
• Occlusions & Shadow Regions: Some areas may be unscanned due to obstructions.
• Motion Distortion: Fast-moving objects cause point cloud misalignment.
• Sensor Saturation: Reflective surfaces (mirrors, water) lead to incorrect depth readings.

Liquid Neural Networks

• Vanishing Gradients: Recursive updates can cause very small gradients, making learning slow.
• Overfitting on Small Data: The model may memorize patterns instead of generalizing.
• Unstable Weights: Due to continuous dynamics, small perturbations can cause large deviations.
• Time-Varying Inputs: The model may fail if trained on static datasets but deployed in dynamic environments.

Vision Transformers (ViTs)

• Requires Large Datasets: Training from scratch needs extensive labeled data.
• Poor Generalization to Small Inputs: Patch embedding fails if input resolution changes significantly.
• Computational Overhead: Large memory requirements can lead to out-of-memory (OOM) errors.
• Misinterpretation of Textures: Unlike CNNs, ViTs may struggle with local textures in images.

14. Test Cases to Verify Correctness

14.1 LiDAR Testing

Test Case 1: Noisy Data Handling

import open3d as o3d
import numpy as np

# Create synthetic noisy point cloud
def test_noise_removal():
    noisy_points = np.random.rand(1000, 3) * 100  # Random noise
    pcd = o3d.geometry.PointCloud()
    pcd.points = o3d.utility.Vector3dVector(noisy_points)

    # Apply statistical outlier removal
    filtered_pcd, _ = pcd.remove_statistical_outlier(nb_neighbors=20, std_ratio=2.0)

    assert len(filtered_pcd.points) < len(pcd.points), "Noise filtering failed"

test_noise_removal()

14.2 Liquid Neural Networks Testing

Test Case 2: Gradient Stability

import torch

# Model with liquid neurons
class LiquidNN(torch.nn.Module):
    def __init__(self):
        super().__init__()
        self.hidden = torch.nn.Linear(1, 10)
        self.output = torch.nn.Linear(10, 1)

    def forward(self, x):
        return self.output(torch.tanh(self.hidden(x)))

# Test stability of gradients
def test_gradient_stability():
    model = LiquidNN()
    input_tensor = torch.tensor([[0.5]], dtype=torch.float32, requires_grad=True)
    
    output = model(input_tensor)
    output.backward()
    
    assert torch.all(input_tensor.grad.abs() < 10), "Unstable gradient detected"

test_gradient_stability()

14.3 Vision Transformer Testing

Test Case 3: Small Input Handling

from transformers import ViTForImageClassification, ViTFeatureExtractor
from PIL import Image
import torch

# Load model
model = ViTForImageClassification.from_pretrained("google/vit-base-patch16-224")
feature_extractor = ViTFeatureExtractor.from_pretrained("google/vit-base-patch16-224")

# Create tiny image
def test_small_image():
    img = Image.new("RGB", (10, 10), (255, 255, 255))  # Very small image
    inputs = feature_extractor(images=img, return_tensors="pt")

    try:
        outputs = model(**inputs)
    except Exception as e:
        assert "size mismatch" in str(e), "Small image handling failed"

test_small_image()

15. Real-World Failure Scenarios

15.1 LiDAR Failures

• Autonomous Vehicles in Fog: LiDAR struggles with low visibility conditions, leading to incomplete perception.
• Highly Reflective Surfaces: Causes false depth readings (e.g., glass buildings).
• Power Failures: Mechanical LiDARs require continuous power and may shut down mid-operation.

15.2 Liquid Neural Network Failures

• Unexpected Time-Varying Inputs: If deployed on real-world data with unseen patterns, the model may output unstable predictions.
• Overfitting in Anomaly Detection: If trained on limited anomalies, the network may fail to detect new ones.
• High Computational Overhead: In embedded systems, real-time inference may be too slow.

15.3 Vision Transformer Failures

• Adversarial Attacks: Small pixel changes can mislead ViTs into incorrect classifications.
• Low-Resolution Images: ViTs struggle if input image patches contain little information.
• Data Efficiency Issues: If not trained with sufficient data, ViTs perform worse than CNNs.

16. Real-World Applications & Industry Use Cases

16.1 LiDAR Applications

• Autonomous Vehicles: Used for 3D environment mapping, obstacle detection, and localization.
• Urban Planning & Mapping: Used in GIS for creating high-resolution maps.
• Robotics & Drones: Enables real-time SLAM (Simultaneous Localization and Mapping).
• Agriculture: Monitors crop health by detecting elevation differences in fields.
• Archaeology: Helps uncover lost civilizations through landscape scanning.

16.2 Liquid Neural Network Applications

• Adaptive AI in Edge Devices: Real-time applications like IoT, drones, and robotic control.
• Financial Market Prediction: Models dynamic time-series data for stock market forecasting.
• Healthcare: Analyzes EEG and ECG data for real-time patient monitoring.
• Cybersecurity: Detects anomalies in network traffic.

16.3 Vision Transformer Applications

• Medical Imaging: Enhances MRI and X-ray diagnostics.
• Satellite Image Processing: Analyzes earth observation data for climate change and urbanization studies.
• Autonomous Vehicles: Assists with road sign recognition, pedestrian detection, and scene understanding.
• Retail & E-Commerce: Improves product search and recommendation systems.

17. Open-Source Implementations

17.1 LiDAR Open-Source Libraries

• Open3D: http://www.open3d.org - A modern library for 3D data processing.
• Point Cloud Library (PCL): https://pointclouds.org/ - Provides algorithms for 3D point cloud processing.
• Autoware: https://github.com/autowarefoundation/autoware - An autonomous driving stack using LiDAR.

17.2 Liquid Neural Network Open-Source Implementations

• Liquid Time-Constant Networks (LTC): https://github.com/mlech26l/torch-ltc - PyTorch-based Liquid Neural Networks.
• MIT's Liquid Neural Networks: https://github.com/mlech26l/torch-liquid - Research-backed implementation.

17.3 Vision Transformer Open-Source Implementations

• Hugging Face ViT: https://huggingface.co/docs/transformers/model_doc/vit - Pre-trained Vision Transformers.
• Timm (PyTorch Image Models): https://github.com/rwightman/pytorch-image-models - Efficient ViT models.

18. Practical Project: Object Detection using LiDAR & Vision Transformers

18.1 Project Overview

This project integrates LiDAR with a Vision Transformer to detect objects in an outdoor environment, such as pedestrians and vehicles.

18.2 Implementation Steps

1. Capture 3D point cloud data using LiDAR.
2. Use Open3D to preprocess the point cloud (filter noise and segment objects).
3. Capture a 2D image of the same scene.
4. Use a Vision Transformer (ViT) to classify objects in the 2D image.
5. Fuse both modalities to improve object detection.

18.3 Code Implementation

Step 1: Load & Preprocess LiDAR Data

import open3d as o3d
import numpy as np

# Load LiDAR point cloud
point_cloud = o3d.io.read_point_cloud("sample.pcd")

# Downsample to reduce noise
downsampled_pcd = point_cloud.voxel_down_sample(voxel_size=0.05)

# Segment objects
plane_model, inliers = downsampled_pcd.segment_plane(distance_threshold=0.02, ransac_n=3, num_iterations=1000)
segmented_objects = downsampled_pcd.select_by_index(inliers, invert=True)

# Visualize results
o3d.visualization.draw_geometries([segmented_objects])

Step 2: Classify Objects using Vision Transformers

from transformers import ViTFeatureExtractor, ViTForImageClassification
from PIL import Image
import torch

# Load pre-trained Vision Transformer
model = ViTForImageClassification.from_pretrained("google/vit-base-patch16-224")
feature_extractor = ViTFeatureExtractor.from_pretrained("google/vit-base-patch16-224")

# Load image
image = Image.open("scene.jpg").convert("RGB")

# Preprocess image
inputs = feature_extractor(images=image, return_tensors="pt")

# Predict objects
with torch.no_grad():
    outputs = model(**inputs)
predicted_class = outputs.logits.argmax(-1).item()

print(f"Detected object class: {predicted_class}")

Step 3: Fusion of LiDAR & Vision Transformer Data

def fuse_data(lidar_objects, image_objects):
    fusion_dict = {}
    for i, obj in enumerate(lidar_objects):
        fusion_dict[f"LiDAR_Object_{i}"] = image_objects
    return fusion_dict

# Example fusion
lidar_objects = ["Vehicle", "Pedestrian"]
image_objects = ["Person", "Car"]

fusion_result = fuse_data(lidar_objects, image_objects)
print(fusion_result)

18.4 Expected Output

• 3D objects extracted from LiDAR.
• Detected object classes from Vision Transformer.
• Final fusion result mapping 3D objects to recognized categories.

18.5 Future Enhancements

• Use YOLO + ViT for more robust object detection.
• Enhance LiDAR object segmentation using deep learning.
• Implement multi-sensor fusion (LiDAR + Radar + Cameras).

19. Competitive Programming & System Design Integration

19.1 Competitive Programming with Advanced Perception

• LiDAR-Based Computational Geometry: Algorithms for point cloud processing in high-dimensional spaces.
• Liquid Neural Networks for Time-Series Analysis: Dynamic AI-driven solutions for pattern recognition tasks.
• Vision Transformers for Image Processing Challenges: Efficient handling of high-dimensional vision tasks.

19.2 System Design Integration

Use Case: Autonomous Vehicle Perception Stack

• LiDAR: 3D point cloud mapping for real-time navigation.
• Liquid Neural Networks: Adaptive control logic for dynamic decision-making.
• Vision Transformers: Real-time object classification and semantic segmentation.

Scalability Considerations

• Microservices Architecture: Distribute perception tasks across independent services.
• Edge Computing: Offload real-time AI computations to specialized hardware.
• Data Caching & Preprocessing: Minimize redundant computations in LiDAR processing.

20. Assignments

20.1 Solve At Least 10 Problems Using These Algorithms

Problem Set:

1. LiDAR Data Filtering: Remove noise from a point cloud dataset.
2. 3D Object Segmentation: Implement RANSAC-based plane segmentation.
3. Path Planning: Use A* search to navigate through a LiDAR-mapped environment.
4. Time-Series Forecasting: Train a Liquid Neural Network to predict stock market trends.
5. Sensor Fusion: Integrate LiDAR and camera data for improved detection.
6. Image Classification with ViT: Train a Vision Transformer to classify images.
7. Object Detection Pipeline: Combine CNNs and ViTs for robust perception.
8. Real-Time AI Inference: Optimize a Liquid Neural Network for edge deployment.
9. Multi-Modal Learning: Build a model that fuses LiDAR, images, and radar.
10. Efficient Processing: Optimize a LiDAR-based system for real-time applications.

20.2 Use in a System Design Problem

Scenario: Smart City Surveillance System

• Problem: Design a surveillance system that monitors traffic, detects anomalies, and ensures pedestrian safety.
• Solution:
  • Use LiDAR for real-time 3D object tracking.
  • Apply Liquid Neural Networks for adaptive incident detection.
  • Leverage Vision Transformers for high-accuracy image analysis.
• Design Considerations:
  • Scalability: Can handle multiple intersections simultaneously.
  • Latency: Uses edge computing for real-time alerts.
  • Reliability: Incorporates redundant data sources (CCTV, LiDAR, sensors).

20.3 Implement Under Time Constraints

• Competitive Challenge: Implement an efficient LiDAR processing pipeline within 1 hour.
• Hackathon Scenario: Deploy a Vision Transformer model for real-time facial recognition in under 3 hours.
• AI Model Optimization Task: Train a Liquid Neural Network in 30 minutes on live time-series data.