Problem Description

With increasing interest in autonomous vehicles in the industry, testing these vehicles becomes more and more important.
Autonomous Vehicles involve a number of subsystems. Primarily, the components can be grouped into Software and Hardware.

The hardware includes the sensors, the wiring, the embedded controllers, and the vehicle itself.

The software includes the controls software, the user interface, and most importantly for an autonomous vehicle – the autonomous driving software (includes the perception, planning, and the decision-making software).

The autonomous driving software needs to be extensively tested to ensure that it addresses the majority of scenarios that any vehicles would encounter. Testing in the real world is difficult. The edge cases that need to be tested include cases where the people or other vehicles in the environment are behaving abnormally. Also in the real world, the safety driver has to stay alert and engage the steering wheel well even in the slightest chance of a calamity. This usually happens much earlier hence the actual “edge case” might not even occur. Simulation helps solve this. But the problem with simulation models is that the behavior of the other people and cars in the simulation is not realistic enough. This is where our product comes in.

Our project aims to build a system where we have realistic models for simulation which have been trained on real-world data. We collect data from the real world and process it to extract the trajectories and behavior of the agents, which is used to build the simulation models.