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, ro
Design and simulate an adaptive path planning system for an autonomous vehicle that operates in unstructured Indian road conditions.
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
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 expected system must 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