VR Simulation for Autonomous Vehicles
Designer, Developer, Researcher
2023
Research, Design, Development, Testing
C++, Rhino, Unreal Engine, Blueprint Visual Scripting
Jidu Auto Advanced Design Team
Overview
This project developed an immersive VR simulation environment in Unreal Engine to visualize and test future autonomous vehicle transit systems. The simulation serves as a strategic tool for Jidu Auto's autonomous vehicle design and urban planning initiatives, enabling stakeholders to experience and evaluate proposed transportation solutions in a realistic virtual environment.
Note: I'm unable to disclose the actual VR environments developed for this project due to company policy, but the summary reflects my role and contributions.
VR Opening Scene
Problem Statement
Autonomous vehicle adoption raises new questions around how advertising, retail, and passenger experience will evolve inside and around self-driving transit systems. Traditional visualization tools often fail to capture how digital billboards, in-car displays, and city-scale ad placements integrate into future autonomous mobility. There was a need for a VR simulation platform that could:
• Visualize advertisement and media spaces within autonomous vehicle interiors
• Model city-scale ad placements across dynamic, self-driving transit networks
• Help designers and stakeholders assess passenger attention, visibility, and engagement
• Support long-term planning of monetization strategies alongside vehicle and city design
Project Gallery
Technical Architecture
Core Technologies
• Unreal Engine 5 - Real-time rendering and physics
• C++ - Core simulation logic and performance-critical systems
• Blueprint Visual Scripting - Rapid prototyping and iteration
• Rhino/Grasshopper - Urban environment modeling
• VR SDK Integration - HTC Vive and Oculus support
Key Features
• Real-time traffic simulation with AI agents
• Dynamic weather and lighting systems
• Multi-user collaborative testing
• Data logging and analytics
• Modular scenario framework
Development Methodology
The project followed an iterative development approach combining rapid prototyping with systematic validation:
Phase 1: Environment Modeling
Created conceptual vehicle interiors and city-scale layouts in Unreal Engine to serve as canvases for testing ad placements, optimizing geometry for real-time rendering.
Phase 2: Traffic Simulation
Prototyped in-car and urban ad placements (screens, panels, billboards) using parameterized layouts. Focused on visibility, user attention, and spatial constraints inside autonomous vehicle cabins and across city corridors.
Phase 3: VR Integration
Implemented immersive VR experiences in Unreal Engine to allow stakeholders to “sit” inside the vehicle or navigate city spaces, evaluating ad exposure and passenger interaction opportunities.
Phase 4: Testing Framework
Delivered VR walkthroughs to design leads and stakeholders, enabling iteration on ad placement strategies and long-term planning for monetization within autonomous transit systems.