Adaptive Path Planning and Collision Avoidance for Autonomous Vehicles on Unstructured Indian Roads
“Most self-driving systems are built for clean, structured foreign roads with proper lane markings and predictable traffic. Indian roads are chaotic, mixed with stray animals, auto-rickshaws, and vehicles driving against traffic without lane discipline. This project requires building a smart navigation simulation system that helps autonomous vehicles safely adapt and maneuver through these messy, unpredictable conditions.”
Deconstructed Problem Blueprint
WHAT TO BUILD?
An adaptive, closed-loop simulation software pipeline developed in MATLAB, Simulink, and RoadRunner that performs multi-sensor perception, non-lane-based trajectory prediction, and real-time path replanning for unstructured environments.
WHO IS IT FOR?
Autonomous vehicle developers, MathWorks researchers, automotive tier-1 suppliers, and ultimately Indian transit authorities and everyday commuters looking for safe automated mobility.
WHY DOES IT EXIST?
Standard autonomous driving algorithms fail on Indian roads because they heavily rely on rigid lane geometries, predictable pedestrian crossings, and orderly traffic queues which do not exist in high-density urban or rural Indian settings.
INPUT → PROCESS → OUTPUT ARCHITECTURE PIPELINE
Synthetic and recorded sensor data (Camera, LiDAR, Radar), Indian Driving Dataset (IDD) logs, custom RoadRunner 3D maps, and ego-vehicle state variables.
Sensor fusion using Automated Driving Toolbox, behavior prediction for erratic agents via Deep Learning Toolbox, decision logic in Stateflow, and dynamic path optimization using Navigation Toolbox and Vehicle Dynamics Blockset.
Real-time collision-free steering and velocity trajectories, simulation execution logs, replanning latency metrics, path smoothness graphs, and 3D closed-loop scenario visualization videos.
CONSTRAINTS
- Must operate without relying on standard lane markings
- Must handle highly irregular, multi-class agent movements (pedestrians crossing anywhere, stray cattle, wrong-side driving)
- Real-time replanning latency limits to ensure safe reactive control
- Strict dependency on MathWorks toolchain toolboxes and workflows
JURY METRICS
- Scenario completion rate across all 5 required Indian test scenarios
- Real-time path replanning latency (milliseconds)
- Path smoothness and jerk optimization metrics
- Collision-free safety distance compliance under mixed-traffic conditions
CORE BOTTLENECK
Accurately modeling and predicting short-term trajectories of highly erratic and non-lane-bound agents (like sudden jaywalkers or wrong-way two-wheelers) without causing excessive false braking or vehicle freezing.
Why You Should Choose This PS (Pros)
- Directly sponsored by MathWorks with clear guidelines on using their exact tool suite
- High social relevance addressing complex real-world AI challenges unique to developing nations
- Visually stunning demo potential using RoadRunner and MATLAB 3D animation scopes
Challenges & Risks to Watch (Cons)
- Steep learning curve for integrating multiple MathWorks toolboxes (Stateflow, Automated Driving, Navigation, Vehicle Dynamics)
- Heavy computation required for running deep learning perception and physics-based simulation concurrently
- Crowded problem statement if teams fail to implement robust custom logic beyond basic tutorials
Recommended Tech Stack & Grand Finale Features
Killer Features to Impress the SIH Jury
View Official SIH 2026 Portal Raw Text▼
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.
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.
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.