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.