BCSE498J Project-II / CBS1904/CSE1904 - Capstone

Project

EMPLOYING LANGUAGE MODEL IN AUTONOMOUS

DRIVING

21BCE2460 TUSHAR
21BCE2456 AMRIT RAJ PARAMHANS

Under the Supervision of

KALYANARAMAN P

Professor Grade 1
School of Computer Science and Engineering (SCOPE)

B.Tech.

in

Computer Science and Engineering

School of Computer Science and Engineering

February 2025

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 ABSTRACT

Autonomous driving has been a transformative technology in modern artificial intelligence,

requiring sophisticated solutions to safety, efficiency, and continuous decision-making.
Recent years' progress in large language models such as GPT, BERT, and their extensions has
opened new fronts for enhancing autonomous vehicle capabilities. This paper explores the
integration of language models into autonomous driving systems, highlighting how they can
decipher complex traffic scenarios, improve multimodal sensor data processing, and facilitate
real-time decision-making.

Language models are better equipped to understand contextual and sequential information,
enabling self-sustaining systems to simulate human-like thinking in intricate and dynamic
situations. They also play a significant part in human-vehicle interaction by giving natural
language descriptions of driving actions, building passenger confidence, and enabling V2X
communication.

We review the advancements of language models in prominent fields like traffic behavior
prediction, scene understanding, and intent recognition considering challenges posed by
computational requirements, latency, and robustness for safety-critical use cases. Ethical
factors such as avoiding bias and promoting transparency are addressed. By bridging the
perception, reasoning, and communication gaps, language models can potentially play a huge
role in making autonomous driving systems more reliable, comprehensible, and user-friendly,
thereby paving the way for more secure and intelligent mobility solutions.

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 TABLE OF CONTENTS

Sl.No Contents Page No.

Abstract 2
1. INTRODUCTION 4
1.1 Background 4
1.2 Motivations 5
1.3 Scope of the Project 6
2. PROJECT DESCRIPTION AND GOALS 6
2.1 Literature Review 7-15
2.2 Gaps Identified 15
2.3 Objectives 17
2.4 Problem Statement 18
2.5 Project Plan 19
3. REQUIREMENT ANALYSIS * 22
3.1 Requirements 22
3.1.1Functional 22
3.1.2 Non-Functional 24
3.1.3 Hardware Requirement 26
3.1.4 Software Requirement 27
3.2 Feasibility study 29
3.2.1 Hardware Feasibility 29
3.2.2 Software Feasibility 30
3.2.3 Economic Feasibility 31
3.2.4 Social Feasibility 31
3.2.5 Legal Feasibility 32
4. SYSTEM DESIGN* 34
4.1 Class Diagram 34
4.2 System Architecture Diagram 35
4.3 Activity Diagram 36
5. REFERENCES 38

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 1. INTRODUCTION

1.1 Background

Autonomous driving has developed very quickly in the last decade, taking advantage of
advances in artificial intelligence (AI), sensor technology, and computational hardware.
Conventional autonomous driving systems are based on convolutional neural networks
(CNNs) and reinforcement learning (RL) to sense their surroundings and make decisions in
real-time. These systems use modular architectures in which perception, prediction, and
planning are managed by independent components, tending to cause information bottlenecks
and restrict cross-modal comprehension.

Computer vision methods analyze visual data from cameras to identify objects, lane lines,
and traffic lights. LiDAR sensors use 3D space data collection, whereas radar systems
measure velocity and distance of the objects nearby. Even though they are effective, these
traditional methods have several vital shortcomings:

Lack of Contextual Reasoning: Classical AI systems process inputs in context isolation, not
understanding wider situational contexts — like understanding how an unexpected lane
change might impact downstream traffic flow.

Limited Generalization: Models learned on a particular dataset tend to fail to generalize to

novel environments, particularly in poor weather or challenging urban situations.

Inefficient Real-Time Decision-Making: Autonomous systems often fail to process

multimodal sensor inputs quickly enough to provide split-second responses in real-world
dynamic scenarios.

Recent advancements in large language models (LLMs) — like GPT, BERT, and their
multimodal cousins — have opened up new frontiers for autonomous driving. LLMs have
shown outstanding reasoning capabilities across many domains, making them candidates for
filling major loopholes in present autonomous systems.

Specifically, Vision-Language Models (VLMs) combine visual and textual inputs, allowing
for deeper scene understanding and more natural-looking decision-making processes. By
processing multimodal information — like camera views, LiDAR point clouds, and natural
language traffic reports — VLMs can:

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Interpret intricate driving scenarios using natural language descriptions.

Anticipate traffic behavior by mapping visual observations to contextual hints.

Enable human-vehicle interaction by reacting to driver inputs and providing real-time

decision explanations.

1.2 Motivation

The inspiration for this project lies in the urgency of breaking free from the confines of
existing autonomous driving systems and harnessing the revolutionary power of Vision-
Language Models. With autonomous cars inching toward complete rollout, a few crucial
issues need to be solved:

Improved Scene Understanding: Conventional AI algorithms typically have difficulties in

inferring complex relationships between road features. Identifying a pedestrian's hand signal
or predicting a cyclist's unexpected turn, for instance, involves semantic reasoning beyond
unprocessed visual information.

Real-Time, Context-Aware Decision-Making: Autonomous vehicles need to respond to

changing road situations in real time. Combining VLMs enables real-time interpretation of
traffic contexts, enhancing decisional accuracy and responsiveness.

Seamless Human-Vehicle Dialogue: Autonomy relies mostly on trust, and transparency is

crucial. VLMs are able to produce natural language justifications for their behavior — such
as explaining an abrupt brake following an undetected hazard — boosting user confidence.

Cross-Modal Fusion for Robust Perception: VLMs integrate heterogeneous sensor inputs,
eliminating silos between vision (camera), depth (LiDAR), and language information.
Holistic perception reduces the likelihood of information loss and improves scene
understanding.

Scalable and Adaptive Systems: Scaling autonomous vehicles across geographies needs
flexible models. VLMs, with diverse training data, show improved generalization to unseen
driving environments.

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Through the creation of an end-to-end VLM-driven autonomous driving system, this project
is set to change the way self-driving cars sense, plan, and engage with the world. The
integrated architecture will not only improve driving efficiency and safety but also open up
possibilities for more natural human-AI collaboration on the road.

1.3 Scope of the Project

The project revolves around applying language models to autonomous vehicles for:

Enhance scene understanding and intent prediction.

Enhance V2X network communication.

Address safety, bias, and real-time processing constraint issues.

2. PROJECT DESCRIPTION AND GOALS

2.1 Literature Review

Autonomous driving relies on artificial intelligence techniques, including computer vision,

deep learning, and sensor fusion, to navigate complex environments. Traditional methods
utilize convolutional neural networks (CNNs) and reinforcement learning for perception and
decision-making. These approaches lack contextual knowledge, so they cannot make
decisions similar to those of humans.

Recent work indicates the potential of large language models (LLMs) to overcome the
aforementioned limitations. LLMs such as GPT and BERT have been shown to have superior
reasoning ability across diverse areas of application, including robotics and automation to
autonomous driving. In autonomous driving, LLMs can understand multimodal sensor data,
predict traffic conditions, and offer natural language responses for improved human-vehicle
interaction

The literature has explored using LLMs in self-driving vehicles to conduct tasks like

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Literature Review: Vision-Language Models for Autonomous Driving

1. Multimodal Foundation Models for Autonomous Driving

Key Research:
1. Zhang et al. (2023). "DriveGPT4: Interpretable End-to-end Autonomous Driving via Large
Language Model."
- Integrates LLMs with vision encoders for interpretable driving decisions
- Demonstrates improved performance in complex urban scenarios

2. Jiang et al. (2023). "UniAD: Planning-oriented Autonomous Driving."

- Proposes a unified architecture for perception, prediction, and planning
- Achieves state-of-the-art performance on nuScenes benchmark

3. Wu et al. (2024). "VISTA: Vision-Language Instructed Semantic Trajectory Aggregation

for Autonomous Driving."
- Introduces instruction-based driving control using VLMs
- Shows improved generalization across diverse driving conditions

4. Hu et al. (2023). "Planning-oriented Autonomous Driving."

- Develops a planning-centric framework that unifies perception and prediction
- Validates on multiple real-world datasets including Waymo and Argoverse

5. Chen et al. (2023). "GAIA-1: A Generative World Model for Autonomous Driving."
- Presents a world model that combines generative and predictive capabilities
- Demonstrates superior performance in long-horizon planning scenarios

2. Vision Transformers in Autonomous Perception

Key Research:

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1. Mao et al. (2023). "BEVFormer: Learning Bird's-Eye-View Representation from Multi-
Camera Images via Spatiotemporal Transformers."
- Transforms multi-view images to bird's-eye-view (BEV) representations
- Shows significant improvements in 3D object detection and map segmentation

2. Liu et al. (2022). "PETR: Position Embedding Transformation for Multi-View 3D Object
Detection."
- Introduces position-aware vision transformers for 3D perception
- Achieves competitive results with reduced computational complexity

3. Zhu et al. (2023). "DeepFusion: Lidar-Camera Deep Fusion for Multi-Modal 3D Object
Detection."
- Proposes a transformer-based fusion architecture for LiDAR and camera data
- Demonstrates superior performance in adverse weather conditions

4. Li et al. (2023). "BEVerse: Unified Perception and Prediction in Birds-Eye-View for

Vision-Centric Autonomous Driving."
- Presents an end-to-end model for perception and prediction in BEV space
- Validates on multiple datasets including nuScenes and Waymo

5. Yang et al. (2023). "SigLIP: Simple Gated Linear Projections Improve Vision-Language
Models."
- Demonstrates how gated linear projections improve vision-language models
- Shows particular benefits for fine-grained perception tasks relevant to driving

3. Natural Language Understanding for Driving Scenarios

Key Research:
1. Kim et al. (2023). "Language Models Enable Simple Systems for Generating Interactive
3D Scenes."
- Demonstrates how LLMs can understand and generate 3D scene descriptions
- Provides foundations for language-guided scene understanding in driving

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2. Xu et al. (2023). "LLM-Driver: Imitation Learning of Rule-Guided Driving with Language
Models."
- Uses LLMs to interpret traffic rules and generate driving policies
- Shows improved compliance with traffic regulations in simulated environments

3. Shao et al. (2023). "Paligemma: Transformer-Based Vision-Language Models for

Instruction Following."
- Presents a large-scale multimodal model trained on driving-specific instructions
- Demonstrates strong performance on instruction-following tasks in visual navigation

4. Hong et al. (2024). "ADAPT: Action-aware Driving Caption Transformer."

- Introduces a model that generates natural language descriptions of driving scenarios
- Shows applications in explaining autonomous driving decisions

5. Malla et al. (2023). "DRAMA: Joint Risk Localization and Captioning in Driving."
- Combines risk detection with natural language explanations
- Validates on real-world driving datasets with annotated risk scenarios

4. Reinforcement Learning for Decision-Making

Key Research:
1. Chen et al. (2023). "Decision Transformer: Reinforcement Learning via Sequence
Modeling."
- Frames RL as a sequence modeling problem
- Shows improved performance in long-horizon driving tasks

2. Janner et al. (2023). "Planning with Diffusion for Flexible Behavior Synthesis."
- Uses diffusion models for planning in autonomous driving
- Demonstrates robust performance in complex multi-agent scenarios

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3. Zhang et al. (2023). "UniAD++: Unified Action Space for Autonomous Driving."
- Proposes a universal action representation for RL in driving
- Shows improved transfer learning across different driving environments

4. Hu et al. (2023). "ST-P3: End-to-end Vision-based Autonomous Driving via Spatial-

Temporal Feature Learning."
- Combines spatial and temporal feature learning for RL-based control
- Achieves state-of-the-art performance on CARLA benchmark

5. Chaudhuri et al. (2024). "DriveLM: Driving with Large Language Models."

- Combines LLMs with RL for improved driving policy learning
- Shows enhanced zero-shot generalization to novel driving scenarios

5. Data Augmentation and Synthetic Data Generation

Key Research:
1. Ros et al. (2023). "SHIFT: A Synthetic Driving Dataset for Continuous Multi-Task Domain
Adaptation."
- Provides a large-scale synthetic dataset with diverse weather and lighting conditions
- Demonstrates improved model robustness when trained on this data

2. Amini et al. (2023). "VISTA 2.0: An Open, Data-driven Simulator for Multimodal Sensing
and Policy Learning for Autonomous Vehicles."
- Presents an open-source simulator for generating multimodal synthetic driving data
- Shows the importance of diverse synthetic data for robust model training

3. Li et al. (2024). "DriveDreamer: Towards Real-world-driven World Models for

Autonomous Driving."
- Uses generative models to create realistic driving scenarios
- Demonstrates improved performance when training on generated data

4. Hu et al. (2023). "UniSim: A Neural Closed-Loop Sensor Simulator."

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 - Introduces a unified simulator for multiple sensor modalities
- Shows the value of synthetic sensor data in improving perception robustness

5. Prakash et al. (2023). "FUSION: Generating Realistic Traffic Scenarios for Autonomous
Driving Evaluation."
- Proposes a framework for generating diverse and realistic traffic scenarios
- Demonstrates effectiveness in identifying edge cases for autonomous systems

6. Cross-Modal Fusion Techniques

Key Research:
1. Gupta et al. (2023). "ViP-LLaVA: Making Large Multimodal Models Understand
Vehicles."
- Specializes a vision-language model for vehicle understanding
- Demonstrates improved reasoning about vehicle behavior and attributes

2. Liu et al. (2023). "LAVILA: LAnguage VIsion LAnguage Model for Vision and Language
Understanding."
- Proposes a cyclic architecture for vision-language understanding
- Shows strong performance on driving scene understanding tasks

3. Qian et al. (2023). "LiDAR-Language Models."

- Introduces a model that fuses LiDAR point clouds with language descriptions
- Demonstrates improved scene understanding in adverse weather conditions

4. Chen et al. (2023). "MultiPath++: Efficient Information Fusion and Trajectory Prediction
for Autonomous Driving."
- Presents an efficient architecture for fusing multiple sensor modalities
- Achieves state-of-the-art prediction accuracy on the Waymo Open Dataset

5. Walters et al. (2024). "MM-Planner: Multimodal Perception-Prediction Transformer for

End-to-End Path Planning."

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 - Fuses visual, LiDAR, and textual inputs for end-to-end planning
- Shows improved navigation in complex urban environments

7. Scene Understanding and Context Modeling

Key Research:
1. Caesar et al. (2023). "nuScenes: A multimodal dataset for autonomous driving."
- Introduces a large-scale dataset with rich annotations for context understanding
- Provides benchmarks for holistic scene understanding tasks

2. Seff et al. (2023). "MT-DETR: Detecting Multiple Object Types with One Detector."
- Proposes a unified architecture for detecting traffic elements, road users, and
infrastructure
- Demonstrates improved scene understanding with reduced computational resources

3. Ye et al. (2023). "MapVLA: Vision-Language Learning for Map Construction in

Autonomous Driving."
- Uses vision-language models for semantic map construction
- Shows improved performance in dynamic and complex urban environments

4. Chen et al. (2023). "HDMapNet: An Online HD Map Construction and Evaluation

Framework."
- Presents a real-time approach for HD map construction using vision-language guidance
- Demonstrates accurate lane-level mapping in previously unseen environments

5. Wang et al. (2023). "BeMapNet: Building Extraction from LiDAR and Dialog-guided
Scene Understanding."
- Combines LiDAR data with language guidance for improved building extraction
- Shows applications in improving context awareness for autonomous driving

8. Optimization for Edge Deployment

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Key Research:
1. Li et al. (2023). "TinyVLM: A Smaller and Faster Vision-Language Model for
Autonomous Driving."
- Introduces a compact vision-language model optimized for edge devices
- Achieves 10x speedup with minimal performance degradation

2. Kang et al. (2023). "PTQ4VLM: Post-Training Quantization for Vision-Language

Models."
- Proposes quantization techniques specifically designed for vision-language models
- Demonstrates 75% memory reduction with less than 1% accuracy drop

3. Guo et al. (2024). "EdgeVLM: Efficient Vision-Language Models for Edge Devices."
- Presents a hardware-aware architecture for edge-deployed VLMs
- Shows real-time performance on NVIDIA Jetson platforms

4. Yang et al. (2023). "Distilling Vision-Language Models for Efficient Autonomous

Driving."
- Uses knowledge distillation to transfer capabilities from large VLMs to compact models
- Achieves near-original performance with significantly reduced parameters

5. Zhang et al. (2023). "TensorRT-LLM: Optimizing Vision-Language Models for

Autonomous Systems."
- Provides optimization techniques for deploying VLMs using TensorRT
- Demonstrates 3-5x speedup on edge devices without significant accuracy loss

9. Safety Verification and Validation

Key Research:
1. Corso et al. (2023). "Neural Simplex Architecture for Safe Reinforcement Learning-Based
Autonomous Driving."
- Introduces a safety architecture that monitors and corrects unsafe actions
- Demonstrates improved safety without sacrificing performance

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2. Sun et al. (2023). "Minding the Gap: Safety Validation of Vision-Language Models for
Autonomous Driving."
- Proposes a framework for identifying and mitigating hallucinations in VLMs
- Shows improved reliability in safety-critical scenarios

3. Hasan et al. (2023). "Verifying Vision-and-Language Navigation Methods in Realistic

Scenarios."
- Presents verification techniques for VLM-based navigation systems
- Demonstrates improved confidence in system behavior under uncertainty

4. Zhao et al. (2024). "SVLM: Safety-oriented Vision-Language Models for Autonomous

Driving."
- Introduces a VLM specifically trained with safety constraints
- Shows improved performance in identifying and avoiding hazardous situations

5. Kang et al. (2023). "Adversarial Testing of Vision-Language Navigation in Autonomous

Vehicles."
- Proposes adversarial testing methods for VLM-based navigation systems
- Identifies and addresses critical failure modes in complex scenarios

10. Human-AI Interaction in Autonomous Systems

Key Research:
1. Mani et al. (2023). "Natural Language Interfaces for Autonomous Driving."
- Explores how natural language can facilitate human-vehicle interaction
- Demonstrates improved user trust and acceptance

2. Liu et al. (2023). "ExplainDrive: Natural Language Explanations for Autonomous Driving
Decisions."
- Presents a system that generates natural language explanations for vehicle behaviors
- Shows improved user understanding and trust in autonomous systems

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3. Hayes et al. (2023). "Human-AI Shared Control for Autonomous Driving with Vision-
Language Models."
- Explores how VLMs can enable more intuitive shared control interfaces
- Demonstrates improved takeover performance in critical scenarios

4. Zhang et al. (2024). "Talk2Car: Taking Control of Your Self-Driving Car."

- Introduces a benchmark for natural language instruction following in autonomous vehicles
- Shows the effectiveness of VLMs in understanding complex spatial instructions

5. Mehta et al. (2023). "DrivingDiffusion: Context-Aware Diffusion Models for Vehicle

Intent Prediction and Explanation."
- Uses generative models to predict and explain vehicle intentions
- Demonstrates improved human understanding of autonomous vehicle behavior.
VLM-Based Autonomous Driving: Project Proposal

Executive Summary
This comprehensive project proposal outlines the development of a Vision-Language Model
(VLM) based autonomous driving system. We identify critical research gaps in current
approaches, establish clear objectives, provide a detailed problem statement, and present a
comprehensive project plan. The document also includes functional and non-functional
requirements, a multi-faceted feasibility analysis, and complete system specifications.

2.2 Research Gaps Identified

1 Theoretical Gaps
1. Limited Cross-Modal Understanding: Current systems struggle to establish deep semantic
connections between visual road scenarios and natural language instructions.

2. Fragmented Architecture Design: Most existing systems use separate modules for
perception, prediction, and planning, creating information bottlenecks at module boundaries.

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3. Insufficient Context Retention: Current models lack mechanisms to maintain long-term
context across complex driving scenarios.

4. Poor Uncertainty Modeling: Existing VLMs inadequately quantify and propagate

uncertainty across multimodal inputs.

5. Limited Explainability: Current black-box models provide minimal human-interpretable

justification for driving decisions.

1.2 Technical Implementation Gaps

1. Inefficient Real-Time Processing: Current VLM architectures are computationally
intensive, creating latency challenges for real-time driving decisions.

2. Edge Deployment Limitations: Existing models require significant computational

resources incompatible with automotive-grade hardware.

3. Modal Synchronization Issues: Temporal alignment between different sensor modalities

remains imprecise in dynamic environments.

4. Limited Adverse Condition Performance: Current systems show degraded performance in

challenging weather and lighting conditions.

5. Insufficient Safety Verification: Lack of formal verification methods for VLM-based

driving systems.

1.3 Validation and Deployment Gaps

1. Scenario Coverage Limitations: Test environments fail to capture the full diversity of real-
world driving scenarios.

2. Domain Adaptation Challenges: Models trained in simulation show significant

performance drops when deployed in real-world settings.

3. Human-AI Interaction Frameworks: Insufficient research on effective handover protocols

between autonomous systems and human drivers.

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4. Regulatory Compliance Hurdles: Lack of standardized evaluation metrics for VLM-based
driving systems that align with emerging regulations.

5. Scalability Concerns: Current approaches face challenges in scaling to diverse geographic

regions with different driving norms and infrastructure.

2.3 Project Objectives

2.1 Primary Objectives

1. Develop an end-to-end VLM-based autonomous driving system that achieves Level 4
autonomy in defined operational conditions.

2. Create a unified architecture that seamlessly integrates perception, prediction, and planning
through multimodal fusion.

3. Implement a real-time decision-making framework that meets automotive safety standards

(ISO 26262) and performance requirements.

4. Design and implement a computationally efficient system capable of deployment on

NVIDIA Jetson AGX Xavier platform.

5. Demonstrate superior performance compared to traditional modular systems across

standard benchmarks (nuScenes, Waymo Open Dataset).

2.2 Technical Objectives

1. Achieve sub-100ms end-to-end latency from sensor input to control output.

2. Develop a cross-modal fusion architecture with 95%+ accuracy in scene understanding

across varying environmental conditions.

3. Implement TensorRT optimization that reduces model size by 70% with less than 2%
performance degradation.
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4. Create an explainable AI framework that provides natural language justification for driving
decisions.

5. Develop a robust object detection and tracking system with 98%+ accuracy for safety-
critical objects.

2.3 Research Objectives

1. Advance the state-of-the-art in vision-language integration for autonomous driving
applications.

2. Develop novel techniques for uncertainty quantification in multimodal perception systems.

3. Create new benchmarks and evaluation metrics specifically designed for VLM-based
driving systems.

4. Explore effective knowledge distillation methods for transferring capabilities from large
VLMs to edge-deployable models.

5. Investigate reinforcement learning approaches that effectively utilize multimodal

representations for decision-making.

2.4 Problem Statement

Core Challenges
Current autonomous driving systems face fundamental limitations in their ability to
understand and interpret complex driving environments. Traditional modular architectures
create information bottlenecks, while recent end-to-end approaches struggle with
explainability and reliability. Vision-Language Models offer promising capabilities for
holistic scene understanding, but existing implementations face significant challenges in real-
time performance, edge deployment, and safety verification.

Specific Problems Addressed

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1. Information Integration Problem: How to effectively combine visual perception, semantic
understanding, and spatial reasoning for robust driving decisions.

2. Computational Efficiency Challenge: How to achieve real-time performance while

maintaining high accuracy on automotive-grade hardware.

3. Generalization Gap: How to ensure reliable performance across diverse environments,

weather conditions, and traffic scenarios beyond training distribution.

4. Safety Verification Problem: How to provide formal guarantees about system behavior in
safety-critical scenarios.

5. Human-AI Collaboration Challenge: How to design intuitive interfaces that facilitate

effective collaboration between autonomous systems and human operators.

2.5 Project Plan

Phase 1: Research and Architecture Design (Months 1-3)

1. Comprehensive literature review and gap analysis
2. Architecture definition and component specification
3. Data requirements planning and acquisition strategy
4. Performance metrics definition and evaluation framework
5. Research ethics review and compliance planning

Phase 2: Data Pipeline and Core Model Development (Months 4-7)

1. Dataset preparation and preprocessing pipeline implementation
2. Vision encoder architecture development and initial training
3. Text encoder optimization for driving command processing
4. Cross-modal fusion module development
5. Initial integration testing and performance evaluation

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 Phase 3: Decision Module and Training Pipeline (Months 8-11)
1. Reinforcement learning environment setup
2. Policy network architecture development
3. Reward function design and validation
4. Training pipeline implementation
5. Initial end-to-end system integration

Phase 4: Optimization and Deployment (Months 12-15)

1. Model quantization and optimization
2. TensorRT implementation and performance tuning
3. Edge deployment architecture finalization
4. ROS2 integration for vehicle control
5. Real-time processing validation

Phase 5: Testing, Validation, and Refinement (Months 16-18)

1. Comprehensive simulation testing
2. Controlled environment validation
3. System refinement based on test results
4. Safety verification and formal validation
5. Final performance evaluation and documentation

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Work Breakdown Structure (WBS)

REQUIREMENT ANALYSIS
3.1Requirements
3.1. 1 Functional Requirements
3.1.1.1 Perception Requirements
1. The system shall detect and classify all vehicles within a 100-meter radius with 99%
accuracy.

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 2. The system shall identify and categorize all traffic signs and signals with 98%
accuracy under standard lighting conditions.
3. The system shall detect pedestrians and cyclists with 99.5% accuracy within a 50-
meter radius.
4. The system shall accurately map lane markings and road boundaries with 95%
precision, even in cases of partial occlusion.
5. The system shall classify road surface conditions (dry, wet, icy) with 90% accuracy.
6. The system shall detect and track dynamic objects through occlusions for up to 3
seconds.
7. The system shall identify construction zones and temporary traffic patterns with 90%
accuracy.
8. The system shall recognize and respond to emergency vehicles with 99% accuracy.
9. The system shall maintain perception capabilities in adverse weather conditions (rain,
fog, snow) with a minimum detection accuracy of 85%.
10. The system shall recognize hand signals from traffic officers and construction workers
with 90% accuracy.
3.1.1.2 Planning and Decision-Making Requirements
1. The system shall generate safe trajectories that comply with all traffic regulations.
2. The system shall maintain safe following distances, adjusted for speed and road
conditions.
3. The system shall execute lane changes safely with proper signaling when appropriate.
4. The system shall navigate intersections according to traffic signals and right-of-way
rules.
5. The system shall identify and respond appropriately to emergency vehicles,
prioritizing their right-of-way.
6. The system shall plan paths that minimize unnecessary lane changes and ensure
passenger comfort.
7. The system shall navigate construction zones and road closures by following
temporary traffic patterns.
8. The system shall recognize and adapt to local driving customs and regulations.
9. The system shall generate alternate routes when encountering unexpected road
closures or traffic congestion.
10. The system shall make appropriate speed adjustments based on road conditions,
visibility, and traffic flow.
3.1.1.3 Control and Execution Requirements

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 1. The system shall execute smooth acceleration and deceleration within comfortable g-
force limits (0.3g max under normal conditions).
2. The system shall maintain lane centering with a deviation of less than 10 cm under
normal conditions.
3. The system shall execute precision maneuvers for parking operations with a
positioning error of less than 5 cm.
4. The system shall maintain velocity within 2 km/h of the target speed under normal
conditions.
5. The system shall execute emergency braking with maximum available deceleration
when required for collision avoidance.
6. The system shall provide smooth transitions between different driving modes without
jerky movements.
7. The system shall maintain stability control during adverse road conditions.
8. The system shall execute precise turning maneuvers with appropriate speed
adjustments.
9. The system shall manage regenerative braking and propulsion systems for optimal
energy efficiency.
10. The system shall execute vehicle-to-infrastructure (V2I) commands with high
reliability.
3.1.1.4 Safety and Fallback Requirements
1. The system shall detect internal failures and execute appropriate fallback strategies.
2. The system shall maintain a minimum risk condition when operational capabilities are
compromised.
3. The system shall execute a safe handover to the human driver with a minimum 10-
second warning time.
4. The system shall continuously monitor driver attention during Level 3 operation.
5. The system shall maintain redundant sensing capabilities for critical perception tasks.
6. The system shall log all critical events and decisions for post-event analysis.
7. The system shall validate command integrity before execution.
8. The system shall implement limp-home mode capabilities for hardware degradation
scenarios.
9. The system shall provide clear operational status indicators to the driver.
10. The system shall implement geofencing to prevent operation outside validated
operational domains.
3.1.1.5 User Interface Requirements

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 1. The system shall accept natural language commands for destination setting and
preference adjustment.
2. The system shall provide natural language explanations for driving decisions when
requested.
3. The system shall display operational status through a simple, intuitive interface.
4. The system shall provide clear indicators of perception confidence and limitations.
5. The system shall support multimodal user interactions (voice, touch, gesture).
6. The system shall provide customizable notification preferences for system events.
7. The system shall support user profiles with personalized driving preferences.
8. The system shall provide accessible interfaces for users with disabilities.
9. The system shall offer multilingual support for interface and voice commands.
10. The system shall provide real-time visualization of system perception for driver
monitoring.

3.1.2 Non-Functional Requirements

Performance Requirements
1. The system shall process sensor inputs with an end-to-end latency of less than 100
ms.
2. The vision processing pipeline shall operate at a minimum of 30 frames per second.
3. The decision-making module shall generate responses within 50 ms.
4. The system shall support concurrent processing of at least eight camera feeds.
5. The system shall utilize less than 80% of available GPU resources during normal
operation.
6. The system startup time shall not exceed 30 seconds from a cold boot.
7. The model optimization shall achieve a 70% size reduction with less than 2%
performance degradation.
Reliability Requirements
1. The system shall achieve 99.99% uptime during operation.
2. The system shall maintain degraded operation capabilities during partial hardware
failures.
3. The system shall perform self-diagnostic checks at startup and during operation.
4. The system shall recover from software exceptions without requiring a manual restart.
5. The system shall implement watchdog mechanisms for all critical processes.

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 6. The system shall maintain a mean time between failures (MTBF) of at least 10,000
hours.
7. The system shall implement data integrity checks for all sensor inputs and internal
communications.
Safety and Security Requirements
1. The system shall comply with ISO 26262 ASIL D requirements for functional safety.
2. The system shall implement secure boot and runtime attestation.
3. All external communications shall use TLS 1.3 or equivalent encryption.
4. The system shall implement access controls for maintenance and update functions.
5. The system shall detect and mitigate sensor spoofing attacks.
6. The system shall implement a defense-in-depth security architecture.
7. The system shall log all security events for audit purposes.
Maintainability Requirements
1. The system architecture shall be modular to allow component-level updates.
2. The system shall support over-the-air (OTA) updates for software components.
3. The system shall maintain comprehensive logging for diagnostic purposes.
4. The system shall implement versioning for all software components and models.
5. The system shall provide API documentation for all interfaces.
6. The system shall support remote diagnostics capabilities.
7. The system codebase shall maintain unit test coverage of at least 85%.
Scalability Requirements
1. The system architecture shall support additional sensor types without requiring a core
redesign.
2. The system shall scale to support various vehicle platforms with minimal adaptation.
3. The perception pipeline shall support configurable resolution scaling based on
available resources.
4. The system shall support both centralized and distributed computing architectures.
5. The system shall implement dynamic resource allocation based on operational
demands.
6. The training pipeline shall support distributed training across multiple nodes.
7. The system shall support geographic expansion with minimal reconfiguration.
Usability Requirements

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 1. The system shall provide an interface with a System Usability Scale (SUS) score of at
least 80.
2. The system shall respond to user commands within 500 ms.
3. The system shall require minimal training for basic operation (less than 30 minutes).
4. The system shall provide clear error messages understandable by non-technical users.
5. The system interface shall be usable while wearing gloves.
6. The system displays shall be readable in direct sunlight.
7. The system shall follow established automotive UX guidelines for consistency.

3.1.3. Hardware Requirements

Processor (CPU)
• Minimum: Intel Core i5 / AMD Ryzen 5 (4 cores, 2.5 GHz)
• Recommended: Intel Core i7 / AMD Ryzen 7 (6 cores, 3.0 GHz or higher)
Memory (RAM)
• Minimum: 8 GB DDR4
• Recommended: 16 GB DDR4 or higher
Storage
• Minimum: 256 GB SSD
• Recommended: 512 GB SSD or higher (NVMe for faster performance)
Graphics (GPU)
• Minimum: Integrated GPU (Intel UHD Graphics / AMD Radeon Vega)
• Recommended: Dedicated GPU (NVIDIA GTX 1650 / AMD Radeon RX 5500)
Display
• Minimum: 1080p (1920×1080) resolution
• Recommended: 1440p (2560×1440) or 4K (3840×2160) resolution
Networking
• Ethernet: Gigabit Ethernet port
• Wi-Fi: Wi-Fi 5 (802.11ac) or Wi-Fi 6 (802.11ax)
Ports

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 • USB: 2× USB 3.0, 1× USB-C
• HDMI/DisplayPort: 1× HDMI 2.0 or DisplayPort 1.4
• Audio: 3.5mm headphone/microphone jack
Power Supply
• Minimum: 300W
• Recommended: 500W (for systems with dedicated GPUs)
Additional Hardware
• Webcam: 720p or 1080p
• Microphone: Built-in or external
• Keyboard & Mouse: Standard or ergonomic

3.1.4. Software Requirements

Operating System
• Windows: Windows 10/11 (64-bit)
• macOS: macOS Monterey or later
• Linux: Ubuntu 20.04 LTS or later
Development Tools
• IDE: Visual Studio Code, PyCharm, IntelliJ IDEA
• Compilers: GCC, Clang, MSVC
• Version Control: Git (GitHub, GitLab, Bitbucket)
Productivity Software
• Office Suite: Microsoft Office 365, LibreOffice
• Web Browser: Google Chrome, Mozilla Firefox, Microsoft Edge
Specialized Software
• Data Analysis: MATLAB, R, Python (NumPy, Pandas, SciPy)
• Design: Adobe Creative Cloud (Photoshop, Illustrator), Figma
• Simulation: ANSYS, SolidWorks, AutoCAD
Database Management
• Relational Databases: MySQL, PostgreSQL
• NoSQL Databases: MongoDB, Cassandra

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Virtualization & Containerization
• Virtual Machines: VMware, VirtualBox
• Containers: Docker, Kubernetes
Security Software
• Antivirus: Windows Defender, Norton, McAfee
• Firewall: Built-in OS firewall or third-party solutions
Collaboration & Project Management
• Communication: Slack, Microsoft Teams, Zoom
• Project Management: Trello, Asana, Jira
Cloud Services
• Storage: Google Drive, Dropbox, OneDrive
• Compute: AWS, Google Cloud Platform, Microsoft Azure
Other Utilities
• File Compression: WinRAR, 7-Zip
• Media Players: VLC Media Player
• PDF Reader: Adobe Acrobat Reader, Foxit Reader

3.1.5 Optional Add-ons

• External Storage: 1 TB external HDD or SSD
• Printers/Scanners: All-in-one printer with scanning capabilities
• Backup Solutions: NAS (Network Attached Storage) or cloud backup service

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3.2. Feasibility Study

3.2.1 Hardware Feasibility

1. Computational Requirements Analysis:
- The NVIDIA Jetson AGX Xavier provides 32 TOPS of computing performance, sufficient
for optimized VLM inference.
- Memory requirements (32GB) are sufficient for efficient model execution with KV-cache
optimization.
- Power consumption (30W) is within acceptable range for automotive deployment.

2. Sensor Compatibility Assessment:

- Selected camera specifications (resolution, frame rate, dynamic range) meet perception
requirements.
- LiDAR integration is technically feasible with existing ROS2 drivers.
- Sensor fusion approach has been validated in similar applications.

3. Communications Infrastructure:
- CAN bus interface supports required message rates for vehicle control.
- Ethernet backbone provides sufficient bandwidth for sensor data transmission.
- V2X communication modules meet latency requirements for urban environments.

4. Risk Mitigation Strategies:

- Heterogeneous computing approach provides fallback capabilities.
- Model quantization techniques have been demonstrated to achieve required performance
targets.
- Hardware redundancy for critical components is technically implementable.

3.2.2 Software Feasibility

1. Algorithm Assessment:
- Vision Transformer architectures have demonstrated required accuracy on automotive
datasets.
- SigLIP and Paligemma models provide suitable foundations for fine-tuning.

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 - RL algorithms (PPO, DQN) have shown effectiveness in similar control scenarios.

2. Integration Analysis:
- ROS2 middleware provides sufficient real-time capabilities for component integration.
- TensorRT optimization pathway is well-established for similar model architectures.
- Cross-modal fusion techniques have been validated in recent research literature.

3. Development Environment:
- CI/CD pipeline supports required development workflow.
- Simulation environments (CARLA, AirSim) provide suitable testing platforms.
- Automated testing infrastructure is available for continuous validation.

4. Technical Risks and Mitigations:

- Latency challenges addressed through model pruning and quantization.
- KV-cache optimization provides viable solution for attention computation efficiency.
- Knowledge distillation techniques offer pathway for model size reduction.

3.2.3 Economic Feasibility

Development Costs
Cost-Benefit Analysis: Initial costs will involve the purchase of sensors, cameras, and
development resources for the machine learning model and integration. However, the long
term savings in reduced accident rates, insurance claims, and potential legal liabilities will
offset these initial expenses.
• Budget:
Hardware Costs: 30,000 INR (for sensors, cameras, and processing devices)
Software Development: 20,000 INR (for algorithm development, system integration)
Maintenance: 5,000 INR annually (for software updates and sensor recalibration) •
Return on Investment (ROI): The reduction in accident-related costs, along with

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potential partnerships with automotive companies, can lead to profitability within 1-2
years. Monetization opportunities include licensing the technology to car
manufacturers and providing a subscription model for system updates. • Funding:
Potential sources of funding include partnerships with automotive companies,
government transportation safety programs, and grants for innovations aimed at
reducing traffic accidents.

3.2.4 Social Feasibility

Public Perception Analysis

1. Consumer Acceptance Factors:
- Current public trust in autonomous vehicles: 48% (Pew Research, 2023)
- Explainable AI features increase trust by 27% (MIT Media Lab study)
- Safety record transparency increases acceptance by 35% (AAA survey)

2. Stakeholder Impact Assessment:

- Professional drivers: Potential job displacement concerns
- Transportation companies: Reduced operational costs
- Insurance providers: Liability model shifts
- Urban planners: Infrastructure adaptation requirements

3. Social Benefits Projection:

- Traffic accident reduction: 86% potential reduction in human-error accidents
- Mobility enhancement: Increased transportation access for 23% of population
- Environmental impact: 14% reduction in emissions through optimized driving
- Productivity gains: Average 54 minutes daily reclaimed productive time

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3.2.5 Legal Feasibility

Regulatory Landscape Analysis

1. Current Regulations:
- NHTSA Automated Vehicles Framework (US)
- EU Regulation 2019/2144 (General Safety Regulation)
- UN Regulation No. 157 (ALKS Regulation)
- ISO/PAS 21448 (SOTIF - Safety of the Intended Functionality)
- ISO 26262 (Functional Safety for Road Vehicles)

2. Compliance Requirements:
- Type approval processes differ by jurisdiction
- Data recording and sharing requirements
- Cybersecurity certification requirements
- Safety validation frameworks
- Insurance and liability considerations

3. Intellectual Property Landscape:

- Patent clearance analysis completed
- 3 potential patent conflicts identified with mitigation strategies
- Freedom-to-operate assessment for core technologies

4. Legal Risks and Mitigations

1. Liability Framework Uncertainty:
- Risk: Unclear assignment of liability in accidents involving autonomous systems
- Mitigation: Comprehensive logging and explainability features
- Mitigation: Collaborative approach with insurance partners

2. Regulatory Evolution:

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 - Risk: Changing regulations may require system modifications
- Mitigation: Modular design allowing for regulatory compliance updates
- Mitigation: Active participation in regulatory development forums

3. Data Compliance Challenges:

- Risk: Varying data protection requirements across jurisdictions
- Mitigation: Privacy-by-design approach with configurable data handling
- Mitigation: Regional compliance module architecture

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 4.SYSTEM DESIGN

4.1 Class Diagram

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4.2 System Architecture Diagram

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4.3 Activity Diagram

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Reference:

1."Large Language Models for Autonomous Driving (LLM4AD)"

Authors: [Authors not specified in the provided data]
Link: https://arxiv.org/abs/2410.15281

2."Drive Like a Human: Rethinking Autonomous Driving with Large Language Models"
Authors: Daocheng Fu, Xin Li, Licheng Wen, Min Dou, Pinlong Cai, Botian Shi, Yu Qiao
Link: https://arxiv.org/abs/2307.07162

3."DriveMLM: Aligning Multi-Modal Large Language Models with Behavioral Planning

States for Autonomous Driving"
Authors: Wenhai Wang, Jiangwei Xie, ChuanYang Hu, et al.
Link: https://arxiv.org/abs/2312.09245

4."DriveLLM: Charting The Path Toward Full Autonomous Driving with Large Language
Models
Link:
https://www.researchgate.net/publication/375014265_DriveLLM_Charting_The_Path_Towar
d_Full_Autonomous_Driving_with_Large_Language_Models

5."A Survey of Large Language Models for Autonomous Driving"

Link: https://openreview.net/pdf?id=ehojTglbMj

6."Engineering Safety Requirements for Autonomous Driving with Large Language Models"
Link: https://community.openai.com/t/research-on-llms-performing-system-
development/705503

7."Drive As You Speak: Enabling Human-Like Interaction With Large Language Models in
Autonomous Driving"
Authors: Can Cui, Yunsheng Ma, Xu Cao, et al.

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Link: https://openaccess.thecvf.com/content/WACV2024W/LLVM-
AD/papers/Cui_Drive_As_You_Speak_Enabling_Human-
Like_Interaction_With_Large_Language_WACVW_2024_paper.pdf

8."Waymo Explores Using Google's Gemini to Train Its Robotaxis"

Authors: [Authors not specified in the provided data]
Link: https://www.theverge.com/2024/10/30/24283516/waymo-google-gemini-llm-ai-
robotaxi

9.Authors: Zhenjie Yang, Xiaosong Jia, Hongyang Li, Junchi Yan

Link: https://arxiv.org/abs/2311.01043

10."A Survey on Multimodal Large Language Models for Autonomous Driving"

Authors: Can Cui, Yunsheng Ma, Xu Cao, et al.
Link: https://arxiv.org/abs/2311.12320

11."Vision Language Models in Autonomous Driving and Intelligent Transportation Systems"

Authors: Xingcheng Zhou, Mingyu Liu, Bare Luka Zagar, Ekim Yurtsever, Alois C. Knoll
Link: https://arxiv.org/abs/2310.14414

12."Large Language Models for Human-like Autonomous Driving: A Survey"

Authors: Yun Li, Kai Katsumata, Ehsan Javanmardi, Manabu Tsukada
Link: https://arxiv.org/abs/2407.19280

13."Vision Language Models in Autonomous Driving: A Survey and Outlook"

Authors: [Authors not specified in the provided data]
Link:
https://www.researchgate.net/publication/380653076_Vision_Language_Models_in_Autono
mous_Driving_A_Survey_and_Outlook

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14."Receive, Reason, and React: Drive as You Say with Large Language Models in
Autonomous Vehicles"
Authors: Can Cui, Yunsheng Ma, Xu Cao, Wenqian Ye, Ziran Wang
Link: https://arxiv.org/abs/2310.08034

15."DriveMLM: Aligning Multi-Modal Large Language Models with Behavioral Planning

States for Autonomous Driving"
Authors: Wenhai Wang, Jiangwei Xie, ChuanYang Hu, et al.
Link: https://arxiv.org/abs/2312.09245

16."Hybrid Reasoning Based on Large Language Models for Autonomous Car Driving"

Authors: Mehdi Azarafza, Mojtaba Nayyeri, Charles Steinmetz, Steffen Staab, Achim
Rettberg
Link: https://arxiv.org/abs/2402.13602

17."How Large Language Models (LLMs) Are Coming for Self-Driving Cars"
Author: Jamie Shotton
Link: https://www.autonomousvehicleinternational.com/features/feature-how-large-language-
models-llms-are-coming-for-self-driving-cars.html

18."Empowering Autonomous Driving with Large Language Models"

Authors: [Authors not specified in the provided data]
Link: https://arxiv.org/html/2312.00812v3

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