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Simulation For Training Autonomous Driving Systems

Self-driving or autonomous driving systems are one of the most promising applications of near-term Artificial Intelligence development and research. This path-breaking research utilizes an immense amount of data - collected while driving - that is labelled and contextually rich.

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Simulation For Training Autonomous Driving Systems

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  1. SIMULATION FOR TRAINING AUTONOMOUS DRIVING SYSTEMS

  2. Self-driving or autonomous driving systems are one of the most promising applications of near-term Artificial Intelligence development and research. This path-breaking research utilizes an immense amount of data - collected while driving - that is labelled and contextually rich.

  3. The levels of driving autonomy have been defined quite clearly by the NHTSA in the USA: Stages of Autonomy in Driving ● Level 0 - No Automation The driver is completely in charge. ● Level 1 - Function Specific Automation Single control functions like speed selection, braking or lane following are automated. ● Level 2 - Combined Function Automation More than one control function is automated. The driver is expected to be available to take over the control of the vehicle at all times and at short notice. ● Level 3 - Limited Self-driving Automation The vehicle remains in control most of the time. The driver is expected to be available to occasionally take over the controls with comfortable transition times. ● Level 4/5 - Full Self-driving Automation The vehicle stays in control all the time. The driver is not required to be available to take over the controls at any time. Vision-based control systems and reinforcement learning became practical and mainstream mainly due to technological developments like deep and recurrent neural networks and unbounded access to world or game interactions.

  4. Methods of Testing Autonomous Functions Virtual Tests / Simulations: These tests encompass the analysis of a large number of scenarios, environments, system configurations and driver characteristics. Proving Ground Tests: For testing the performance of driving robots and self-driving cars during critical manoeuvers. Field Tests: For the investigation of real driving situations and calibrating system specifications. Simulation testing is crucial to efficiently verify the huge amounts of functional requirements!

  5. Latest Developments in the Testing of Autonomous Vehicles • Methods: • Greater simulation for verification of control algorithms and traffic rule compliance. • Structured search for functional deficits, instead of waiting for them to arise during test driving • Functions: • Continuously assessing and adapting to external conditions and rules • Reliably judging if the limits of vehicle autonomy are close • Announcing the end of the autonomous mode early enough that the driver can take over (Level 3 Autonomy) • Bringing the vehicle to a safe stop (in Level 3 Autonomy) if the driver fails to take over

  6. Major Reasons for Accidents during Autonomous Driving Frequent simulation of such situations is critical to the mitigation of these causes of accidents.

  7. Challenging Traffic Situations Recreated in Simulations A number of virtual models need to be developed in order to simulate these situations.

  8. Virtual Models Required for Driving Simulation Platforms: These models need to be original and generate realistic as well as unique situations to train autonomous driving systems.

  9. Conclusion Advanced Driver Assistance Systems (ADAS) are the precursor for autonomous driving. drivebuddyAI’s ADAS technology has also been tested on the simulators discussed in this article, in addition to proving-ground and on-road field tests. The data utilized in the simulator is specific to Indian conditions - which are highly complex. It is quite a challenge to create a standard, global and robust AI that can work in such complex scenarios - drivebuddyAI’s innovation in its ADAS and AI technology can lead us to autonomous vehicles sooner! Before autonomous vehicles can drive you anywhere, they have to prove that they will not drive you into trouble. A huge number of test driving kilometres will be needed to attain this level of reliability and safety. This is not practically and economically possible or sensible, which is why simulations are needed to train these autonomous driving systems.

  10. About drivebuddyAI drivebuddyAI is a fleet management system with AI technology, designed to provide visibility and help with actionable insights to improve operations - from driving safety to driver selection & driver coaching. drivebuddyAI’s technology brings with it a driver drowsiness detection system, driver fatigue detection, and a collision warning system for increased driving safety. Visit drivebuddyai.co for further information!

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