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You will get autonomous vehicles predictable AI model
Project details
The two technologies that will be used for this project are visual detection and lidar data. Visual detection is the process of detecting traffic signs and lights in real time, while Lidar data is used to detect pedestrians and other obstacles.
In terms of performance, visual detection is more accurate than Lidar data. This is because Lidar data can sometimes be obstructed by objects such as trees or buildings. However, Lidar data is more reliable in terms of safety, as it can provide a more detailed view of the surrounding environment.
Overall, both technologies are necessary for an autonomous vehicle to operate safely and efficiently.
In terms of performance, visual detection is more accurate than Lidar data. This is because Lidar data can sometimes be obstructed by objects such as trees or buildings. However, Lidar data is more reliable in terms of safety, as it can provide a more detailed view of the surrounding environment.
Overall, both technologies are necessary for an autonomous vehicle to operate safely and efficiently.
What's included $1,990
These options are included with the project scope.
$1,990
- Delivery Time 20 days
- Number of Revisions 1
- Number of Model Variations 1
- Number of Scenarios 2
- Number of Graphs/Charts 2
- Model Validation/Testing
- Model Documentation
About Dongchan
AI Engineer | CUDA Inference Optimization | RAG/LLM Agent Developer
Guangzhou, China - 9:31 am local time
**What I deliver:**
- CUDA Inference Optimization: Built Ember, a 12,000-line multi-GPU inference engine achieving 33.9 tokens/sec on Qwen3-8B with dual RTX 3080 Ti. Open source at github
- RAG/Agent Development: Shipped multiple commercial systems including AI consultants and knowledge assistants handling 1,000+ daily API calls
- Full-Stack AI: 8+ years of software development. Can take your project from concept to production independently
**Tech stack:** Python, C++, CUDA, LangChain, Qdrant, GPT/Claude APIs, Go
Currently pursuing M.S. in AI (Hawaii Pacific University), focused on LLM inference optimization.
I work async-friendly hours (GMT+8) and communicate clearly in written English.
Let's discuss your project.
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After purchasing the project, send requirements so Dongchan can start the project.
Delivery time starts when Dongchan receives requirements from you.
Dongchan works on your project following the steps below.
Revisions may occur after the delivery date.
Data preparation
Get the training data 2. Data cleaning 3. Feature selection 4. Data Transforms
Write an AI/deep learning domain-specific language
Defines the constructs for this AI model