Why Bytes is Betting on Vision-First AI to Make Two-Wheelers Safer
- BYTES
- Oct 22
- 6 min read
Roughly speaking, the question that shaped BYTES is simple: if cars deserve intelligent safety systems, why not bikes? In the past decade, cars have leapt ahead with lane-keeping assist, blind-spot monitoring, and automatic emergency braking becoming standard across much of the world. Two-wheelers, meanwhile, remain stuck in time, helmets and ABS are still the only meaningful safety interventions, even though bikes account for the majority of road fatalities in India and across Asia. At BYTES, we believe a delivery rider, a student, or a commuter should not get anything less than the kind of anticipatory safety that cars now enjoy. The answer isn’t a single sensor or a marketing promise, it’s a platform decision. We chose vision-first perception powered by a purpose-built AI because that’s the fastest, most scalable path to bring meaningful safety to millions of two-wheelers.
Below we explain why vision is the right foundation, what makes our AI different, how we turn camera pixels into life-saving decisions, and what the practical roadmap looks like, from fleets to OEMs and everywhere in between.
The problem: motorcycling is a semantics problem, not just a distance problem
Motorcycles are small, nimble, and everywhere, which makes them both essential and vulnerable. Many modern vehicle sensors are great at saying “something’s there” and “how far,” but they are terrible at answering the question riders actually need:
Is that something about to collide with me?
Two technical realities make motorcycles special:
Lean, vibration, and posture change the camera/sensor geometry constantly. A system that works at 0° attitude may fail at 20° lean.
Risk is semantic. Brake lights, turn intention, stopped vehicles, overtakes, and potholes require understanding, not just distance.
That leads to a crucial design principle: the safety stack must be able to interpret the scene and predict likely futures, not just beep when it sees an object.
Why Not Radar or LiDAR, and Where Vision Wins
Radar and LiDAR are powerful sensing technologies, but they’re the wrong foundation for scalable, mass-market two-wheeler safety.
The first challenge is platform stability. Two-wheelers are not stable sensing platforms. A typical motorcycle can lean up to 55° in a corner and endure 20–30 Hz chassis vibration at highway speeds. Radar and LiDAR rely on narrow beam alignment and static calibration; even a small ±5° misalignment can cause more than 20% ranging error, or worse, completely miss an object as it slips into the beam’s sidelobe. Cameras, by contrast, perceive the entire scene holistically. With IMU fusion and visual odometry, a vision-based system can dynamically re-project the environment in real time, maintaining situational awareness even during aggressive lean or vibration.
The second issue is meaning versus geometry. Radar and LiDAR return raw geometry, they can tell you that something exists 22 meters ahead moving at −3 m/s, but not what that object is or why it matters. Vision, however, decodes semantics, it understands context. It can detect brake-light activation, turn-indicator frequency, rider posture, lane drift, road texture, and even potholes or debris. These are the same cues that human riders rely on to infer intent, not just distance.
A third, often overlooked limitation is false detections or “ghost objects.” In dense urban traffic, radar signals bounce off multiple reflective surfaces, cars, signboards, barriers, even wet roads. This multipath reflection creates phantom echoes, causing the radar to detect objects that aren’t really there. These false positives can be mitigated through tuning and sensor fusion, but they remain a persistent challenge in close-quarter, high-density environments typical of Indian cities. Vision systems, however, rely on direct optical cues, inherently filtering out such ghost artifacts by interpreting consistent visual evidence rather than reflections.
The next factor is practicality and scalability. Cameras are compact (<30 grams), easily mounted on mirrors, fenders, or headlamps, and require no moving parts. A single ECU can process multiple camera streams simultaneously, for detection, depth estimation, and object tracking, making vision systems both modular and scalable.
Finally, while LiDAR remains prohibitively expensive, radar is indeed a cost-competitive with cameras, but cost alone doesn’t close the gap. Radar still lacks semantic understanding and struggles with two-wheeler dynamics, especially under lean or in dense, reflective traffic. Cameras, paired with intelligent AI models, bridge this gap, combining affordability with meaning, enabling true, context-aware safety for every rider.
Parameter | Vision (Camera + AI) | Radar | LiDAR |
Output Type | Semantics + Geometry | Geometry Only | Geometry Only (3D Mapping) |
Key Strength | Context understanding (detects meaning and intent) | Accurate distance measurement | High-resolution 3D mapping |
Two-Wheeler Compatibility | ✅ High - Handles lean angles & vibration | ⚠️ Moderate - Beam misalignment under lean | ❌ Low - Highly sensitive to tilt and vibration |
Cost & Scalability | 💸 Low - Mass-producible, retrofit-ready | 💸💸 Moderate - Affordable but complex calibration | 💸💸💸💸 High - Expensive sensors & maintenance |
Form Factor | Compact & flexible - camera modules, easy mount | Bulky modules - limited mounting flexibility | Fragile rotating parts, heavy & delicate |
Core Limitation | Low-light noise, glare sensitivity | Semantic blindness - can’t infer meaning | Cost, fragility, and maintenance complexity |
The BYTES Approach: Vision + Proprietary AI
Choosing cameras is only step one. What matters is how intelligently those pixels are interpreted.
At BYTES, our proprietary AI turns raw video into real-time awareness, understanding not just where objects are, but what they mean for the rider.
Our Blind-Spot Monitoring uses a context-conditioned spatial model that defines adaptive Regions of Interest (ROIs), dynamic zones that reshape continuously based on:
Speed: Expands from 2.5 m → 6.5 m laterally (20 → 80 km/h) for earlier detection.
Lane curvature: Warps ROIs using IMU yaw-rate to maintain coverage while leaning.
Traffic density: Suppresses diverging vehicles, escalates converging ones (>0.5 m/s).
The outcome: >95% detection accuracy and <3% false alerts, even under vibration, lean, or night conditions.
Field results:
Urban / ≤60 km/h → Recall 92–96%, False Alerts ≤0.2/hr
Highway / 60–100 km/h → Recall 92–96%, False Alerts ≤0.1/hr
Night / Rain → Recall 90–92%, False Alerts 0.3–0.6/hr
Our Collision Alert predicts risks up to T+2 seconds ahead, about 45 m at 80 km/h, giving riders a crucial 1.8-second reaction margin to brake or maneuver.
By fusing rider intent (turns, steering, lean data) with environmental tracking, Bytes keeps alerts smart, not noisy.
And it all runs edge-optimized, delivering sub-40ms inference on ECUs drawing under 10W, built for scale, built for India.
Making Vision Work Where It Used to Fail
Vision systems aren’t perfect, and motorcycles are the hardest testbed for them. We engineered around those limits.
Motorcycles experience 15–35 Hz vibration and 2–6 g acceleration spikes, which can easily distort frames and corrupt optical flow. We counter this across multiple layers:
Hardware: Ruggedized, aluminum-damped camera mounts rated to 50 g shock.
Firmware: Electronic image stabilization and rolling-shutter correction with <5 ms latency.
Training: Motion-blur augmentation and synthetic jitter, improving temporal tracking stability by +27% on vibration-heavy datasets.
Rain, fog, and night rides drastically reduce signal-to-noise ratio and contrast, so our visual pipeline adapts.
HDR fusion (3-exposure stack) handles high-dynamic scenes.
Auto-exposure tuning (EV ±3 range) uses histogram equalization for clarity under variable lighting.
Dedicated low-light models, trained on 200+ hours of night-ride footage, improve mean average precision by +18%.
Finally, we built a proprietary dataset from Indian roads, day, night, rain, and chaos included, to capture the edge cases others miss. Every deployment feeds back new data, and every mile on the road makes our model smarter.
How the System Learns and Evolves
Every BYTES-equipped vehicle doesn’t just react, it learns.
Each ride, each alert, each kilometer helps the system understand India’s roads a little better.
Our vision platform continuously gathers real-world cues:
Road conditions: detecting potholes, uneven surfaces, and lane quality.
Traffic behavior: spotting near-misses, sudden lane changes, and braking patterns that reveal risky zones.
Environmental context: adapting to night rides, rain, fog, and glare through data captured in real traffic.
All this information feeds back into our AI models, making them sharper, faster, and more reliable with every deployment. The more bike on the road, the more diverse scenarios our system learns from, from city intersections to rural highways.
Every kilometer ridden makes the next ride safer.
Final Thought: Affordable, Semantic Safety for the Many
Two principles shape BYTE’S product DNA:
The best safety is anticipatory and semantic.
True impact comes only through affordability and retrofitability.
Cameras unlock semantics. Our AI converts semantics into actionable predictions. Fleets and riders, in turn, provide the scale of data that makes the system more reliable with every ride.
Just as helmets and ABS were once resisted but eventually mandated, the next logical step is a safety layer that understands the world around a rider, and intervenes before a small lapse becomes a fatal crash.
That’s why BYTES is building a vision-first, AI-native platform: to make this level of safety real, affordable, and inevitable.
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