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Smart Bicycle Camera & Radar System Architecture The proposed system combines a high-performance RockPi 3588S2 host with front and rear camera/light modules, a head-unit display, and a radar module. The RockPi 3588S2 (an 8-core ARM platform with on-board NPU[1]) will perform 4K video recording and AI inference. On the front handlebar a Camera+Light module will capture stabilized 4K video; a single cable carries its data and power to the Head Unit. The head unit (a rugged 5–5.5″ outdoor touchscreen with multi-touch) handles user controls and displays data, while passing along power and signals to the main compute unit. On the rear, a Camera+Light+Radar module mounts behind the seat: this houses a 4K camera, LED light, and a 24 or 60 or 77GHz Doppler radar (like Garmin’s Varia) to detect approaching vehicles. A single cable from the rear module runs to the central compute unit. All video, sensor and control signals converge on the RockPi unit for processing and logging. The hardware components will be chosen for durability and production readiness. For example, Sony STARVIS IMX415 8MP sensors (as used in Radxa’s 4K camera) can capture 3840×2160 video (up to ~90 fps) with excellent low-light performance. Each camera module should include electronic image stabilization (EIS) – for instance, 6-axis EIS like Cycliq’s Fly6 Pro – to smooth out bumps. The head-unit will use a sunlight-readable capacitive LCD (5–5.5″, ~720×1280 or higher) with an interface (e.g. FPC cable) providing touch, power, and display signals in one harness. The radar module will employ a cycle-rated rear radar (detects vehicles up to ~140 m, 6–99 mph) that reports range/velocity of multiple objects. Power distribution and serial or CAN wiring (BEL microcontroller) will control lights and read radar; Wi-Fi or USB can offload video and diagnostics. Cameras and Imaging 4K camera uses a Sony IMX415 sensor (8 MP, 3840×2160, Starvis™ low-light) with on-board ISP. The design will use two IMX415-based cameras (front and rear) with wide-angle optics. Each camera must support 4K loop recording to on-board flash, auto-exposure and auto-white-balance via its ISP (as in e-con’s USB camera). To achieve stabilization, each module will include a small IMU (gyro/accel) and EIS algorithm – similar to high-end bike cams (e.g. Cycliq Fly6 Pro’s 6-axis EIS) – so that rough-road vibration is digitally compensated. Data from each camera will be captured via the RockPi’s MIPI CSI interface. The RK3588S2 platform provides a 4-lane MIPI CSI input, sufficient for 4K@30fps video. If necessary, USB3 interfaces can also be used (the IMX415 cams are USB UVC-compliant). The firmware will ensure Linux drivers (V4L2) and libraries (GStreamer/OpenCV) are available for 4K capture. In prototype stage, off-the-shelf camera modules can be used; the final design will use custom enclosures rated for outdoor use. Head Unit (Display & Controls) The Head Unit is a bicycle computer-like interface mounted on the handlebar. It will use a 5–5.5″ sunlight-readable touchscreen (capacitive multi-touch, IP65-rated, e.g. 700+ nit brightness). This display interfaces to the RockPi via an FPC/HDMI uses one ribbon for power, video and touch, but smaller custom panels are available. The head unit firmware will drive a basic UI (speed, status indicators, settings) on this screen. Navigation (GPS) and status menus will also appear here. For prototyping, an HDMI LCD with a simple graphics stack can be used; final device will have a more polished GUI. All touch and rotary encoder inputs, lighting controls, and accessory signals are routed through this unit, minimizing cables on the handlebar. Compute Unit (RockPi 3588S2 Platform) At the core is the RockPi 3588S2 board (RK3588S2 SoC). This provides a 64-bit octa-core CPU (Cortex-A76/A55) and 8–32 GB RAM, ample for multi-camera video and AI. It can encode/decode 4K@60 video and supports Linux/Android OS. Importantly, the RK3588S2 has a built-in neural processing unit (NPU) that enables on-device DNN inference. This NPU (up to ~6 TOPS) supports INT8/16 and FP16 precision, so YOLO-based object detection can run efficiently without offloading to cloud. We will leverage Rockchip’s RKNN toolkit to convert trained YOLO models to run natively on the NPU. The compute unit firmware (Linux) will handle camera capture, radar input, logging, and system control. It will run a circular buffer recorder for both cameras (auto-loop 4K video to SD/eMMC), with incident-lock capability. The system must provide real-time alerts: combining radar returns with YOLO image classification. Network connectivity (Wi-Fi) will allow video offload and OTA updates later. A GPS module interfaces via USB/serial or integrated GPS chip for navigation. Peripheral interfaces (GPIO, CAN/UART) will connect to BEL light controller and sensors. Radar and AI Integration For rear-object detection, the system fuses radar data with camera vision. The radar will detect up to ~8 objects to 140 m. Its Doppler measurements give closing speed (note Garmin Varia’s limitation: objects at same speed as bike are not detected). The rear camera feeds frames to a YOLO-based neural net (using e.g. YOLOv7/v8) to classify object type (car, truck, cyclist, etc.) in real time. YOLO (“You Only Look Once”) is a fast single-shot detector widely used for real-time video[13]. By combining radar (distance/speed) with YOLO (semantic label), the system can display more informative alerts (“Car at 60 m, 90 km/h”) instead of a generic blip. This also reduces false-positives: for example, YOLO can identify if a detected object is a cyclist rather than a threatening vehicle. Later phases will add front-camera analysis (e.g. “car door open ahead” or turn signal flash). But initially only the rear camera runs AI. The RockPi’s NPU will run the YOLO inference (after converting the model to RKNN[11]). We will verify that a YOLO model can meet latency requirements on the NPU – if not, model pruning or lower-res inference may be needed. It is assumed feasible; if the freelancer determines it is not possible, they must report this early (YOLO on low-power boards can be challenging without acceleration). User Interface & App Integration The head-unit display will show live status: alerts from radar+AI, current speed (from GPS or bike sensor), camera status, and allow settings (light modes, recording). A minimal built-in GUI will be coded (e.g. using Qt or LVGL) that shows simple text/graphics. Initially this can be a “raw” interface (like HDMI screen) with diagnostic overlays. Ultimately the UI will evolve (by a designer) into a polished bike-computer-like dashboard. For user control, buttons or touch on the head unit will set modes (lights on/off, recording on/off). In future phases, Bluetooth/Wi-Fi connectivity will pair to a mobile app: bike status APIs (light mode, battery, etc.) can be exposed, and OTA firmware updates supported (common in IoT products). The firmware should include a secure bootloader and API endpoints for settings. Development Phases and Milestones The project will proceed in iterative phases, each with clear deliverables: 1. Hardware Setup & POC Board Configuration – Finalize hardware: use fixed RockPi 3588S2 board (board has been finalized as Kharas Edge2). Select/procure camera modules (Sony IMX415 boards) and lens, a ~5″ touchscreen, a 77GHz bike radar, BEL light controller. Verify Linux support for each component (camera drivers, radar interface, display). Set up basic power/cabling prototype. Milestone: Working dev board capturing video from both cameras. 2. Lights Control Integration – Interface the BEL microcontroller to RockPi. Implement switching/PWM control of front/rear lights. Ensure handlebar controls (through touchscreen) are read by the system. Milestone: Lights toggled on/off via firmware/API. 3. 4K Dashcam Functionality – Develop looped recording for front and rear cameras. Test continuous 4K@30 (or 1080p@60) recording to storage. Implement lock-on-impact (if IMU triggers or manual) to save incidents. Ensure image stabilization is active. Milestone: Simultaneous front/rear loop recording with proof-of-concept playback. 4. Basic Head Unit Display – Drive the touchscreen with simple outputs: e.g. show “Dashcam active” text and a menu to adjust settings. At this stage, the UI is primitive (like direct HDMI output). Confirm user can start/stop recording and see speed/GPS fix. Milestone: Operable head-unit showing diagnostics and responding to input. 5. YOLO Object Classification (Rear) – Integrate a YOLO model (e.g. YOLOv7) for rear-camera. Fine-tune on road objects (cars, cyclists, buses). Note: detailed fine-tuning is not included, but baseline performance must be demonstrated. Fuse with radar: when radar flags an object, run YOLO on recent frame to classify. Display result (“Car approaching 50m”). Milestone: Rear camera + radar working together: when vehicle detected, its type is recognized. 6. Enhanced Display/UI – Improve on-screen UI: show graphical alerts, classified icons, and live video preview if needed. This includes basic navigation (using GPS) to show speed/heading. A very simple route map or compass can be added. Milestone: Head unit shows camera/radar alerts and basic navigation info via user-friendly screens. 7. GPS Integration – Interface a GPS module (or use phone GPS over Bluetooth). Display current location, speed, and (eventually) turn-by-turn on the UI. Log GPS position with video for mapping. Milestone: GPS coordinates logged; speed and simple route info visible on screen. 8. Advanced AI Features – Extend computer vision to front camera tasks (e.g. detect opening car doors, oncoming turns). Add object detection for hazards ahead. These can be prototyped but are future stretch goals. Milestone: Prototype code flags a “door opening” event using front camera (proof of concept). 9. Radar & App Finalization – Finalize radar calibration and object alert logic. Begin development of companion mobile app and OTA mechanism. Ensure system can connect to a smartphone to send logs and receive settings. Milestone: System streams sample data over Wi-Fi; accept a firmware update via network (test mode). 10. Pilot Testing & Documentation – Conduct on-bike field tests, refine mounting hardware, and write complete documentation (including detailed design, bill of materials, and testing results). Milestone: Hardware meets durability and performance targets; full report with diagrams. Each phase should conclude with a demonstration and code review. Throughout, keep design modular so that initial prototype parts (e.g. dev kits, evaluation modules) can be replaced with production-worthy components later. Key risks that the freelancer must address upfront: ensuring Linux drivers support the IMX415 cameras and 4K capture on the RK3588S2, and confirming that YOLO inference can run fast enough on the NPU for real-time alerts. If these are not feasible, the freelancer should report this before deep development. Disclaimers: Remote Execution & Physical Handling • The Freelancer will operate fully remotely. All design, research, documentation, and firmware/software development will be conducted online. • Physical activities (e.g., sourcing hardware samples, assembling modules, cabling, mounting on bicycle frames, conducting field tests, environmental tests, and IP67 validation) will be carried out in Canada by the Client. • The Freelancer may provide build instructions, wiring diagrams, and test procedures, but will not physically perform assembly, soldering, or installation. • All field feedback, measurements, photos, logs, and test results will be collected by the Client and shared with the Freelancer for debugging, tuning, and validation. • The Freelancer will rely on the Client’s feedback loop for hardware bring-up, prototype verification, and environmental validation. • Any delays or issues arising due to logistics, hardware shipping, or differences between simulated/test environments and the Client’s physical tests are outside the Freelancer’s responsibility.