AI System for Self-Driving SUV

@AZTURK1

Hi there, I understand you need an end-to-end AI/ML stack to enable lane keeping, tight-space automatic parking and early pedestrian detection that runs in real-time on an automotive-grade computer. I have strong production ML + robotics experience and will deliver clean, commented Python/C++ code that plugs into your simulator and vehicle pipeline. - End-to-end model architecture and training scripts (perception + control, modular for iterative retraining) - Trained weights + documented performance metrics for lane keeping, parking manoeuvres, and pedestrian detection - ROS-compatible integration wrapper, deployment profile for an automotive-grade ECU, and a brief test report with simulation results Skills: ✅ Object Detection ✅ Python ✅ Computer Vision ✅ Machine Learning (ML) ✅ Robotics ✅ Data Augmentation Certificates: ✅ Microsoft® Certified: MCSA | MCSE | MCT ✅ cPanel® & WHM Certified CWSA-2 I will use camera + LiDAR sensor fusion (or radar fallback) and validate with staged simulation → small-vehicle tests, include rollback and safety checks, deterministic logging and CI tests. Ready to start; delivery: $12,500 in 1 day. Which automotive-grade computer (make/model) and ROS distribution/version does your pipeline use, and do you prefer a camera+LiDAR fusion or camera+radar baseline for the initial release? Best regards,

@SmartCodeDev

Hi, this sounds like a solid applied autonomous driving ML project. I’ve worked on several computer vision + robotics pipelines involving real-time detection, multi-sensor perception, and edge deployment. For problems like this I usually build the perception stack with PyTorch and detection models such as YOLOv8 or Detectron2, depending on latency constraints. Sensor fusion and vehicle integration are typically handled through Robot Operating System, which makes it straightforward to connect perception, planning, and control modules inside the simulation and the test vehicle. For your three core features: • Lane keeping – vision-based lane detection + lightweight control model (PID or learning-based controller). • Pedestrian detection – real-time object detection with early warning thresholds and safety margins. • Auto parking – surround perception + trajectory planning with classical planners or learned policies. The stack usually includes camera + LiDAR fusion, temporal filtering, and optimized inference so it runs in real time on automotive hardware (Jetson / similar edge systems). I’d deliver the full training pipeline, model weights, ROS-compatible integration layer, and simulation validation report, along with clear documentation for hyperparameters, dataset prep, and augmentation so the models can be retrained or extended later. Happy to review your simulation setup and test vehicle pipeline to make sure the architecture plugs in cleanly before training starts.

@lokdeshwark

As an experienced professional in full-stack development with a strong command over Python, I am well-suited to take on your AI system for the self-driving SUV project. I bring to the table not only my technical proficiency but also my commitment to delivering top-quality work. My solutions have always centered on clean code and robust documentation, precisely what you need for this complex project. Previously, I have worked on projects involving autonomous driving modules and computer vision tasks—elements that align precisely with your requirements for this project. I understand the importance of reliable autonomous functionality packed within tight spaces and the need for quick iterations before any road trials. Drawing upon my comprehensive understanding of ROS and similar middlewares, I can seamlessly integrate your perception and control models into these systems for assured functionality. Moreover, my experience also includes working with cloud computing and AWS deployment which might come handy as we move forward into real-time testing on an automotive-grade computer. My approach has always been iterative, grounded in realistic timelines, and with an unyielding focus on providing high-quality results that help drive business growth which is exactly what you're looking for here. Let, 's turn our ideas into a revolutionary AI/ML system together!

@Wizztron

I recently delivered a project with this exact scope, focusing on seamless integration of perception and control models for autonomous vehicles. Your need for clean, professional, and user-friendly code that reliably handles lane keeping, automatic parking, and pedestrian recognition aligns perfectly with my approach to developing well-documented, automated AI/ML solutions. I specialize in sensor fusion using camera and LiDAR data, real-time model deployment on embedded automotive-grade computers, and delivering integrated systems compatible with middleware like ROS. While I am new to freelancer, I have tons of experience and have done other projects off site. I would love to chat more about your project! Regards, MN Williams

@vedansh4422

I’m a strong fit for this project because I combine hands-on AI/ML system building with real-world deployment thinking. I’ve designed and integrated deep learning models into production-ready pipelines, including real-time computer vision systems using CNNs and multimodal inputs. My experience includes training, optimizing, and deploying models that must perform reliably under latency constraints—exactly what an automotive-grade environment demands. For your SUV platform, I can architect a modular perception stack (camera + LiDAR/radar fusion), implement lane detection and pedestrian recognition using state-of-the-art detection/segmentation networks, and design a robust control layer (PID/MPC-based) for lane keeping and precise parking maneuvers. I prioritize clean, well-documented Python/C++ code, reproducible training scripts, and clear performance reporting with measurable metrics (mAP, IoU, latency benchmarks). Since you already have simulation and a test vehicle, I’ll ensure ROS-ready integration and iterative validation. My focus is reliability, safety, and real-time efficiency—turning research-grade models into deployable autonomous driving modules.

@Fahadghouri7

As a well-rounded and experienced AI developer who specializes in Python, I believe I'm an ideal match for your project. Not only have I worked on similar road trials, but I've also been trained to fuse sensor data, develop AI models and integrate the systems into ROS or similar middleware. My robust skill set combined with the real-world experience of my code running in real-time on automotive-grade computers can help turn your ambitious ideas into a smooth, high-performance reality. One of the highlights of my work is my ability to deliver clean, well-commented code that allows easy understanding even for non-technical team members. This aligns perfectly with your requirement of clear explanations regarding hyperparameters choices and data-augmentation techniques. Moreover, my familiarity with simulation environments will enable us to iterate quickly, making sure every element is optimized before we move to the road trials. From end-to-end model architecture and training scripts to detailed test reports that outline possibilities for improvement— you can count on accurate and timely deliverables from me. Let's collaborate and declare success over the finish line together!

@khrystynad4

Hello, Your project requires a tightly integrated perception and control stack suitable for real-time autonomous vehicle deployment. I can support development across perception, sensor fusion, and vehicle control, structured for rapid iteration within your simulation and ROS pipeline. Proposed Technical Approach Perception: • Pedestrian detection using a lightweight real-time detector (YOLOv8/RT-DETR optimized with TensorRT) • Lane detection via hybrid deep learning + classical CV (segmentation + polynomial fitting) • Sensor fusion using camera + LiDAR (EKF-based fusion for robust detection) Control: • Lane keeping via Pure Pursuit or MPC controller • Automatic parking using trajectory planning + low-speed closed-loop control Integration: • ROS2-compatible nodes • C++ inference wrapper for real-time execution • Python training pipeline with reproducible configs Deliverables: • Full training scripts • Documented hyperparameters & augmentation strategy • Trained weights + benchmark metrics • Integration wrapper • Simulation validation report Estimated timeline: 6–8 weeks Estimated budget: $35,00 (milestone-based) I prioritize safety, deterministic behavior, and reproducibility. Happy to review your simulation environment and hardware constraints before finalizing scope. Best regards, Khrystyna