SIH26037: Adaptive Path Planning and Collision Avoidance for Autonomous Vehicles on Unstructured Ind — SIH 2026

SIH26037: Adaptive Path Planning and Collision Avoidance for Autonomous Vehicles on Unstructured Ind — SIH 2026

What they'll ask you

9

Generated from the brief's own wording and the competition's published rules — never from a guess about what this ministry prefers.

Has any part of this been shown at a previous event, hackathon or college project?
The guidelines are explicit: your solution must not have appeared in any previous event or programme, of any sort. A recycled project is what a team under time pressure reaches for.
The brief asks for simulation pipeline. How would you build that?
Decoded from SIH26037 itself — A working model in MATLAB and Simulink that integrates perception, prediction, path planning, decision logic, and vehicle motion. The brief asks for it by name.
The brief asks for scenario design. How would you build that?
Decoded from SIH26037 itself — Driving scenarios created using RoadRunner that represent Indian road conditions, including village roads and urban intersections. The brief asks for it by name.

6 more— the ones a panel gets to if there is time

The brief asks for perception and prediction. How would you build that?
Decoded from SIH26037 itself — Components to identify diverse road users and predict the short-term motion of irregular movement patterns. The brief asks for it by name.
The brief asks for path planning and control. How would you build that?
Decoded from SIH26037 itself — A system using Navigation Toolbox and Stateflow that generates safe, collision-free paths and replans in real time. The brief asks for it by name.
The brief asks for results and documentation. How would you build that?
Decoded from SIH26037 itself — Performance metrics such as replanning latency, path smoothness, and scenario completion rate, along with a technical report and demonstration video. The brief asks for it by name.
Why not use what already exists? Name the closest thing to this that is already running.
A team that has not named the alternative themselves is answering this for the first time in the room.
Which single thing will you demonstrate end to end, start to finish, with nothing skipped?
Ours, not a rule: a narrow thing that fully works survives questioning better than a broad thing that half works. If nobody on the team can name it, that is the finding.
Show me this working: evaluators will check if you tested your solution across all five required scenarios, used the suggested MathWorks tools, and provided specific metrics like replanning latency, path smoothness, and scenario completion rate.
This is the evaluator read for your problem statement, decoded from the brief's own wording.

Take this to an AI

Pick what you are about to do and copy the prompt. It carries the organisers' own wording, the constraints they never spell out, and an instruction not to invent requirements they never set.

Who has this problem, what already exists, and what you would have to find out.

# Context: India's Smart India Hackathon 2026

I am on a student team entering SIH 2026. Problem statement SIH26037, set by MathWorks.
Its official title, word for word: "Adaptive Path Planning and Collision Avoidance for Autonomous Vehicles on Unstructured Indian Roads"
Theme: Smart Vehicles. Category: software.

In plain terms: A MATLAB and Simulink simulation that tests an autonomous vehicle navigating unstructured Indian roads with mixed traffic, unexpected obstacles, and missing lane markings.

## The organisers' full description, verbatim

Background: Most autonomous driving systems are developed for roads with clear lane markings, standard signage, predictable traffic flow, and controlled intersections. Indian roads are often very different. Vehicles of many types share the same space, including cars, buses, trucks, auto-rickshaws, twowheelers, bicycles, pedestrians, pushcarts, and animals. Drivers and pedestrians may change direction suddenly, merge without signalling, drive against traffic, or cross at unmarked locations. In many areas, road edges are unclear, potholes are common, and formal lane discipline is limited. These conditions make it difficult for traditional path planning methods that depend on structured road geometry and predictable motion. India has a large and diverse road network that includes village roads, crowded market areas, urban intersections, and highways. To support the safe deployment of autonomous vehicles in such environments, students must build planning systems that can adapt in real time to uncertainty, mixed traffic, and changing road conditions. Description: Design and simulate an adaptive path planning system for an autonomous vehicle that operates in unstructured Indian road conditions. The system should perceive the environment using a multi-sensor setup such as camera, LiDAR, and radar, and identify diverse road users and obstacles, including auto-rickshaws, pushcarts, pedestrians, and animals. It should predict the short-term motion of surrounding agents, including non-lane-based and irregular movement patterns, and generate a safe, collision-free path that can be replanned in real time. The solution should also handle practical driving situations such as missing lane markings, informal merging, sudden pedestrian movement, and unexpected obstacles on the road. Teams should validate their solution using at least five realistic Indian road scenarios, such as an unmarked village road, a busy urban intersection without signals, a highway merge involving slow-moving vehicles, a dense market area with mixed traffic, and a sudden cattle-crossing event. Teams are encouraged to use MathWorks tools such as RoadRunner for scenario design, Automated Driving Toolbox for sensor modeling and fusion, Navigation Toolbox and Stateflow for planning and decision logic, Vehicle Dynamics Blockset or a Simulink bicycle model for vehicle behavior, and Deep Learning Toolbox for detection and trajectory prediction. Expected Solution: The expected solution should include three main parts.First, teams should build a working simulation pipeline that integrates perception, prediction, path planning, decision logic, and vehicle motion in MATLAB and Simulink. Second, teams should create realistic driving scenarios that represent Indian road conditions, including at least two detailed RoadRunner scenes such as a village road and an urban intersection, and use them to test the vehicle across all five required scenarios. Third, teams should present results that show safe and reliable navigation, including collision-free performance, smooth path generation, and timely replanning during changing road conditions. The final submission should include the simulation model, the designed scenarios, performance results with metrics such as replanning latency, path smoothness, and scenario completion rate, a short technical report that explains the approach and design choices, and a demonstration video that shows the vehicle navigating the test scenarios. The solution should demonstrate closed-loop validation of autonomous driving behavior under realistic mixed-traffic conditions.
## Constraints that are not negotiable

- The judged build is a prototype demonstrated in roughly 36 hours by students, not a product.
- A jury evaluates it against this problem statement's own wording, not against general merit.
- We submit on sih.gov.in; the problem statement number and title above must match exactly.
- Your solution must not have appeared in any previous event or programme, of any sort.
- The organisers attached a dataset, so build against theirs rather than inventing one.

## What I want from you

1. Who actually has this problem in India, and what they do about it today.
2. What already exists — government systems, open-source projects, published research — that we should not rebuild.
3. For each item under 'what the brief never says', what the realistic answer is likely to be and how I could verify it.
4. The two or three things about this domain that a student team would get wrong without local knowledge.
## How to answer

- Everything above between quotation marks is the organisers' own wording. Treat it as the requirement.
- Do NOT invent requirements the brief does not state. If something matters and is not stated, say so and ask me, rather than assuming.
- Where you are inferring rather than reading, mark it as your inference.
- Do not guess what this ministry wants from its name. Its wording above is the only evidence.

---
Problem statement facts assembled by Wynit from MathWorks's published brief for SIH 2026.
The plain-English summary, the list of parts and the evaluator read are our interpretation of that text; the quoted title and the full description are theirs.

The government's original description (500 words)

Background: Most autonomous driving systems are developed for roads with clear lane markings, standard signage, predictable traffic flow, and controlled intersections. Indian roads are often very different. Vehicles of many types share the same space, including cars, buses, trucks, auto-rickshaws, twowheelers, bicycles, pedestrians, pushcarts, and animals. Drivers and pedestrians may change direction suddenly, merge without signalling, drive against traffic, or cross at unmarked locations. In many areas, road edges are unclear, potholes are common, and formal lane discipline is limited. These conditions make it difficult for traditional path planning methods that depend on structured road geometry and predictable motion. India has a large and diverse road network that includes village roads, crowded market areas, urban intersections, and highways. To support the safe deployment of autonomous vehicles in such environments, students must build planning systems that can adapt in real time to uncertainty, mixed traffic, and changing road conditions. Description: Design and simulate an adaptive path planning system for an autonomous vehicle that operates in unstructured Indian road conditions. The system should perceive the environment using a multi-sensor setup such as camera, LiDAR, and radar, and identify diverse road users and obstacles, including auto-rickshaws, pushcarts, pedestrians, and animals. It should predict the short-term motion of surrounding agents, including non-lane-based and irregular movement patterns, and generate a safe, collision-free path that can be replanned in real time. The solution should also handle practical driving situations such as missing lane markings, informal merging, sudden pedestrian movement, and unexpected obstacles on the road. Teams should validate their solution using at least five realistic Indian road scenarios, such as an unmarked village road, a busy urban intersection without signals, a highway merge involving slow-moving vehicles, a dense market area with mixed traffic, and a sudden cattle-crossing event. Teams are encouraged to use MathWorks tools such as RoadRunner for scenario design, Automated Driving Toolbox for sensor modeling and fusion, Navigation Toolbox and Stateflow for planning and decision logic, Vehicle Dynamics Blockset or a Simulink bicycle model for vehicle behavior, and Deep Learning Toolbox for detection and trajectory prediction. Expected Solution: The expected solution should include three main parts.First, teams should build a working simulation pipeline that integrates perception, prediction, path planning, decision logic, and vehicle motion in MATLAB and Simulink. Second, teams should create realistic driving scenarios that represent Indian road conditions, including at least two detailed RoadRunner scenes such as a village road and an urban intersection, and use them to test the vehicle across all five required scenarios. Third, teams should present results that show safe and reliable navigation, including collision-free performance, smooth path generation, and timely replanning during changing road conditions. The final submission should include the simulation model, the designed scenarios, performance results with metrics such as replanning latency, path smoothness, and scenario completion rate, a short technical report that explains the approach and design choices, and a demonstration video that shows the vehicle navigating the test scenarios. The solution should demonstrate closed-loop validation of autonomous driving behavior under realistic mixed-traffic conditions.