Motorcycles, e-bikes, and other two-wheelers are finally getting the kind of assistance features that cars have had for years: blind-spot alerts, forward collision warnings, lane drift warnings, proximity alerts from behind, and more.
This category is often called ARAS or ADAS for riders (Advanced Rider Assistance Systems / Advanced Driver Assistance Systems) and it’s built around one simple idea: the bike is always watching what the rider can’t.
The core of these systems is vision. Put a smart camera on the front of the bike, another on the rear, add a small onboard processor (the “brain”), and you can continuously monitor traffic, distance, approaching vehicles, pedestrians, and dangerous situations. When something looks risky, the system warns the rider immediately, through a light, a symbol on the dash, a beep, or a haptic signal.
This is not about recording GoPro footage. This is about giving the rider a second set of eyes, eyes that never blink, never look down, and never miss the van sitting in the blind spot.
What rider assistance actually does
Most modern two-wheeler safety systems built on cameras and AI aim to deliver a handful of high-value warnings:
1. Blind-spot detection
Side- and rear-facing vision continuously checks for vehicles where mirrors don’t always help, the classic “car in the blind spot” problem. When the system sees something creeping into that danger zone, it can warn the rider before they lean or change lanes.
2. Forward collision warning
A forward-facing camera looks at what’s ahead and estimates relative speed. If the vehicle in front brakes hard or the gap is closing too quickly, it can warn the rider to react sooner.
3. Headway / following distance monitoring
This is about space management. The system measures the distance to the vehicle in front and tells the rider when they’re following too closely. On higher-end platforms this can be linked to adaptive cruise or speed limiting.
4. Lane / drift alerting
On larger motorcycles and high-speed use, the system can detect lane markings and warn if the bike is unintentionally drifting out of lane. On smaller vehicles, a similar idea is used to detect general lateral movement or weaving that looks unsafe.
5. Rear threat awareness
Many close calls for cyclists and motorcyclists don’t come from the front — they come from behind. A rear camera can track vehicles approaching too fast, flag aggressive overtakes, and warn if a car is closing in too close.
6. Vulnerable road user detection
On the flip side, the system doesn’t just watch cars. It can also identify pedestrians and other cyclists in your path and alert you if someone steps out or cuts across your line.
Put simply: the system is constantly answering the question “Am I about to be hit?” and telling the rider early enough to do something about it.
Different hardware approaches (and why they matter)
Not every bike needs a huge, expensive onboard computer. There are now multiple architectures to get these safety features onto two-wheelers, and each one targets a different cost/complexity level.
Let’s walk through the main ones.
1. Smart sensor / all-in-one camera
This is the new wave. Instead of a “dumb” camera that just sends video to some big box computer, the camera itself has a built-in AI processor.
A good example of this class is Sony’s IMX500. It’s not just an image sensor, it’s an image sensor plus a neural network accelerator in the same package. That means the sensor can run an object detection model directly on the chip. It doesn’t need to stream raw video somewhere else to be analyzed. It can simply say “car detected on left rear” or “pedestrian ahead, 12 meters.”
Why this matters for two-wheelers:
- No bulky compute module required.
- Low power, which is critical for e-bikes and light electric vehicles.
- Low latency: the alert is generated right where the pixels are captured.
- Low bandwidth: instead of streaming video, it just sends the detection result.
This model is perfect for lightweight safety add-ons: you mount the smart camera on the handlebar or rear rack, and you now have real-time situational awareness without a full ECU.
In other words: the camera isn’t just a camera anymore. It’s already the brain.
2. Camera + external ECU (the “camera plus brain” model)
This is the architecture the motorcycle world is already familiar with.
Here, the camera module does what cameras do best: capture a clean, high-dynamic-range (HDR) image in all lighting conditions. Then that image is sent over a high-speed link (for example MIPI CSI-2 or A-PHY) to a small onboard computer, an ECU dedicated to rider assistance.
That ECU runs the AI: object detection, lane tracking, distance estimation, blind-spot logic, etc. It also ties into the rest of the vehicle. It can:
- talk to the bike over CAN bus,
- drive a warning light or a dashboard icon,
- combine feeds from multiple cameras (front + rear + side),
- log near-miss events or “almost crashes” for later analysis.
Why this matters:
- You can use more powerful models, because you’re no longer limited to what fits on one sensor.
- You can fuse multiple camera views, not just one.
- You can integrate tightly with the vehicle’s own systems and displays.
On the camera side, this setup typically uses advanced automotive-grade sensors such as Sony’s new high-resolution HDR sensors. These newer sensors are designed to see detail in very bright and very dark areas at the same time, think “low sun in your face and a dark tunnel entrance,” or “wet asphalt at night with LED headlights behind you.” They’re built for harsh lighting and fast motion, which is exactly what riders experience.
This model is common for motorcycles and high-end e-mobility platforms, where you’ve got a bit more room for a small ECU under the fairing, in the tail, or in the head unit.
3. Full multi-camera safety cluster
This is the premium version.
In this setup you don’t just have a camera and a brain. You have:
- multiple rugged cameras (front, rear, sometimes side),
- a central compute unit that’s doing continuous perception,
- and a rider interface: a dash display or integrated HMI that shows alerts and status live.
The rider gets visual alerts right in the instrument cluster: vehicle in blind spot, closing speed warning, pedestrian ahead, etc. The system may also store incidents (for evidence, insurance, or rider coaching) and support software updates so new detection features can be rolled out.
This is where the technology starts to feel like high-end automotive ADAS, but scaled to two wheels.
Where Camemake fits in
Camemake operates in the vision layer of this ecosystem: the cameras and the connection between the cameras and the “brain.”
Our focus is to make those vision blocks as strong and as easy to integrate as possible for two-wheel platforms.
Here’s what that looks like in practice:
High dynamic range imaging
Two-wheelers deal with brutal lighting: sun glare, tunnel entry, headlights at night, reflective road paint in rain. Camemake camera modules are built around sensors that offer very high dynamic range, so you still get usable, structured images in those conditions instead of a blown-out white smear or a black blob. That’s the difference between “maybe there’s a car there” and “there is a car there, 2 meters behind you.”
IR handling and filtering
Street lighting, brake lights, LEDs, and sunlight all dump infrared into the lens. If you don’t manage that, you get ghosting, false positives, and color washout. We tune for that. We can provide IR-cut or IR-pass options depending on use case (day visibility vs. night assist), and we calibrate the pipeline so models get clean data, not noisy flare.
Compact, rugged modules
On a two-wheeler, space is never “available,” it’s something you create. Our camera modules are built on compact MIPI CSI-2 designs with very small lens barrels (M12/M8 style, fisheye, panoramic, etc.) so they can live in a fairing, a mirror housing, even a lighting assembly. They’re vibration-ready and thermally stable, so they survive riding, not just lab benches.
Low-latency path into compute
We deliver video directly into the SoC ISP (image signal processor) on common embedded platforms like NVIDIA Jetson, Raspberry Pi-based compute, custom boards, etc. That means low latency, low overhead, and predictable behavior for the neural networks that sit on top.
Ready for the ECU or ready to be the ECU
You can use a Camemake camera in a classic “camera + ECU” architecture. The camera connects to your IRAS ECU (Intelligent Rider Assistance ECU), which runs your perception stack and talks to your CAN bus and display.
Or you can go lighter: pair our optics and tuning with a smart vision sensor that already has an AI accelerator on the die (like an IMX500-class approach). In that case, the camera is basically also the computer. For an OEM building safety features into an e-bike or lightweight scooter, that’s very attractive: minimal wiring, minimal housing volume, minimal weight.
Fast integration for OEMs
We ship not just the sensor board but the bring-up: drivers, tuning, calibration, and reference mechanical options. That lets an OEM or integrator drop vision into the product without becoming a camera company. You get a path to production instead of a science experiment.
What happens next
We’re at the point where camera systems on two-wheelers aren’t just “dashcams with ambition.” They’re active safety devices.
A few big shifts are happening at the same time:
- Cameras are getting smarter. Instead of just sending video, the camera can already say “car in blind spot now.” That’s a fundamental change.
- The compute is getting smaller. You don’t need a giant automotive-grade box anymore. You can get meaningful ARAS behavior with a sensor + a few TOPS of edge compute instead of a full GPU rack.
- Lighting is no longer an excuse. High dynamic range sensors and tuned IR handling mean the system can see in situations where a human struggles — tunnels, backlit intersections, harsh rain at night.
- Integration is maturing. We’re moving from “strap this accessory on your handlebar” to “this is part of the bike.” Cameras are being designed directly into headlight housings, tail sections, instrument clusters, and CAN-connected ECUs.
- Fusion is coming. Vision will start pairing with radar, inertial sensors, maybe even simple lidar. The bike becomes aware not just of what it sees, but also of what it feels and what it senses around corners.
The endgame is simple: give riders the kind of early-warning intelligence that drivers of modern cars already get, but packaged for a world where size, power, cost, and weather exposure are all much tighter constraints.
That’s why this space is moving fast. That’s why we’re investing in vision. And that’s why two-wheel safety is finally catching up to four-wheel safety, not by copying the car, but by building something that’s purpose-built for the rider.