Adaptive Path Planning and Collision Avoidance for Autonomous Vehicles on Unstructured Indian Roads

The receipts 🧾

Every red flag on this statement, in full. These are the four places it bites.

1. Exhibit A

   Licence coverage for Automated Driving Toolbox, RoadRunner and Deep Learning Toolbox is not universal on campus installations — confirm you can actually launch these tools before you commit, because the entire statement assumes them

2. It gets worse

   RoadRunner scene authoring is slow and unfamiliar, and two detailed scenes are a hard requirement, so budget days rather than hours for content creation that produces no algorithmic progress

3. Still reading?

   Autonomous driving attracts the strongest and most numerous entries of any theme, so being competent here is not enough — your prediction of irregular non-lane-based motion is the differentiator, not the planner

4. And the finisher

   Simulation-only validation means you never demonstrate real-world performance, and a judge asking how your perception behaves on genuine IDD footage rather than synthetic RoadRunner renders has a fair and difficult question

The damage report 📉

Every score this statement earned, and what each one actually costs you.

• Feasibility

  4/5

  Actually buildable, which on this slate is rarer than it sounds. Do not squander it on scope.

  This is entirely simulation so there is no vehicle, no sensor and no track to arrange, the Indian Driving Dataset is public and linked, and the toolchain the statement names is exactly the toolchain the sponsor provides — the only real gate is whether your institution's licence covers Automated Driving Toolbox, RoadRunner and Deep Learning Toolbox.

• Innovation scope

  3/5

  Mildly interesting. The novelty will not carry the room; the build has to.

  The pipeline stages, the five validation scenarios, the reported metrics and even the recommended toolchain are all specified, so your genuine design freedom is concentrated in the planner formulation and the prediction model for non-lane-based motion.

• Clarity

  5/5

  The ask is unambiguous, which quietly removes your favourite excuse.

  Exceptionally well written for a hackathon statement — it names the five required scenarios individually, splits the expected solution into three explicit parts, states the three evaluation metrics, and lists the exact submission artifacts down to the demonstration video.

• Acceptance potential

  3/5

  Middle of the pack. This statement will not win the room for you — you will have to.

  The specification, the linked datasets and the prescribed metrics are all in its favour, but autonomous driving is one of the most attractive-sounding statements on the portal so the field will be crowded with strong MATLAB teams, and the scope is genuinely enormous for the time available.

• Effort

  Massive

  A semester of work wearing a hackathon costume. Something is getting cut; decide what now, not in week five.

  Perception, prediction, planning, decision logic and vehicle dynamics integrated into one closed loop, plus two detailed RoadRunner scenes and five validated scenarios, plus a metrics study, technical report and demo video, is several projects stacked together and RoadRunner scene authoring alone has a steep learning curve.

• Demo-ability

  Easy

  Easy to demo — and so is everyone else's. Working is the floor here, not the achievement.

  A simulated vehicle threading through a market or braking for a cow is immediately legible to any judge without explanation, and the required demonstration video means the strongest visual moment is a mandated deliverable rather than an afterthought.

The demo they will have already seen 🪞

Somewhere around 80–180 teams are heading here, and the description is doing the choosing for most of them. They will read the same brief, reach the same architecture, and build a version of the same demo you are planning. Being correct is the floor. If your five minutes could be swapped with the team before you and nobody in the room would notice, you have not picked badly — you have built predictably, which costs exactly the same and hurts more.

What survives 🛡️

The ground worth standing on when the questions start.

• The evaluation metrics are named in the statement — replanning latency, path smoothness, scenario completion rate — so you know exactly what you will be measured on, which is rare and enormously valuable
• The Indian Driving Dataset is public, linked and genuinely captures the mixed traffic the statement describes, so your detector trains on the actual conditions rather than on European road footage
• Pure simulation means no hardware, no track access and no safety approvals, so the whole project is executable from a laptop