See the approach
A camera captures the lighting context and the approaching vehicle.
AutoDim
HARDWARE INTEGRATION / COMPUTER VISIONECS team project · presented at SETCON
A little less glare. A more thoughtful response.
THE CONTEXT
An ECS team prototype connecting oncoming-vehicle detection to headlight control.
THE KEY DECISION
Use camera input and a Raspberry Pi to drive a physical beam-switching relay.
THE RESULT
An integrated hardware/software prototype presented at SETCON under faculty guidance.
AutoDimPERCEPTION → RESPONSE
MECHANISM STUDY / 12SCHEMATIC
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Interpret the scene
Image-recognition logic on a Raspberry Pi Zero W evaluates the input.
Change the beam
A relay translates the software decision into a high/low headlight switch.
AutoDim: Automatic High/Low Beam Transition System is an ECS team project connecting computer vision to headlight control. Developed at VIT-AP, it turns a camera observation into a physical relay action.
PROJECT TYPE
ECS team project · vision & hardware integration
FOCUS
Hardware integration + Computer vision + Research
STATUS
ECS team project · presented at SETCON
/01
The question.
High-beam glare can make night driving uncomfortable. A useful automatic response must connect the visual context of oncoming traffic to the vehicle’s lighting mechanism.
/02
The approach.
A camera supplies image input to a Raspberry Pi Zero W. Image-recognition logic evaluates light intensity and oncoming vehicles, then a relay mechanism switches the headlight beam. The project links perception, decision and actuation in one hardware/software prototype.
/03
What came out of it.
The team developed the prototype from August to October 2024 under the guidance of Dr. Pradosh Ranjan Sahoo at VIT-AP University and presented it at SETCON, the Student Engineering Technical Conference.
01
From pixels to a physical response
The core integration spans a camera, on-device analysis and an electrical relay. The final output is a change in the lighting circuit.
02
Context before switching
The project explores camera-based recognition of oncoming vehicles and light intensity, giving the decision stage image context alongside brightness.
03
An academic team effort
Developed collaboratively at VIT-AP and presented at SETCON. This case study describes the shared system and its hardware/software integration.
Under the surface.
- Visual input
- Camera · light-intensity and vehicle recognition
- On-device decision
- Raspberry Pi Zero W · image-recognition logic
- Physical output
- Relay mechanism · high/low beam control
SCOPE & LIMITS
- Academic team prototype; automotive certification or production installation is not claimed.
- The beam change is an illustrated mechanism, not measured optical performance.
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