Decision technologies of simulation to reality for autonomous driving:a survey
Since the mid-1980s,numerous research institutions have been developing autonomous driving technologies.The main idea of autonomous driving technology is to perceive the ego-vehicle states and its surroundings in real time through sensors,utilize an intelligent system for decision-making planning,and execute the driving operation through the control system.The decision-making module,which is an important component in autonomous driving systems,bridges perception and vehicle control.This module is mainly responsible for finding optimal paths or correct and reliable behav-iors for the ego-vehicle to effectively drive on the road.In the research process of autonomous driving decision-making tech-nologies,which are remarkably strict for safety,if the training is performed directly in the real world,then it will not only lead to a considerable cost increment but will also miss some marginal driving scenarios.In this case,numerous studies are first conducted in the simulation world before applying new autonomous driving models in the real world.However,the simulation can only provide an approximate model of vehicle dynamics and its interaction with the surrounding environ-ment,and the vehicle agent trained only in the simulation world cannot be generalized to the real world.A gap still exists between reality and simulation,which is called the reality gap(RG)and poses a challenge for the transfer of developed autonomous driving models from simulated vehicles to real vehicles.Researchers have proposed numerous approaches to addressing the reality gap.This paper presents the principles and state-of-the-art methods of transferring knowledge from simulation to reality(sim2real)and parallel intelligence(PI),as well as their applications in decision-making for autono-mous driving.Sim2real approaches reduce RG by simply transferring the learned models from the simulation to the reality environment.In autonomous driving,the basic idea of sim2real is to train the vehicle agent in the simulation environment and then transfer it to the reality environment using various methods,which can substantially reduce the number of interac-tions between the vehicle agent and the reality environment.Sim2real can also improve the effectiveness and performance of decision-making algorithms for autonomous driving.At present,the main sim2real methods include robust reinforcement learning(RL),meta-learning,curriculum learning,knowledge distillation,and transfer learning,as well as some other helpful techniques such as domain randomization and system identification,which have their own way of reducing the real-ity gap.For example,transfer learning bridges the reality gap by directly addressing the differences between domains.Vehicle agents in the real world may be exposed to problems that do not exist in the simulation world;thus,some research-ers use meta-learning to bridge the gap.Sim2real methods handle the RG problem in some way,but their computational cost remains a challenge,especially when dealing with complex and dynamic environments,which limits the application range of sim2real methods.The PI,which solves the RG problem by parallelly performing the simulation environment with the reality environment,is proposed to solve the aforementioned problem.PI is a new paradigm based on the ACP method(artificial society,computational experiment,and parallel execution),which deeply integrates simulated and real sce-narios.The main process of parallel intelligence involves the formation of a complete system through repeated interactions between the artificial and physical systems and the reduction of the RG through parallel execution and computational experi-ments.Among them,the computational experiment is divided into description learning,prediction learning,and prescrip-tive learning,which gradually transitions from the simulation environment to the real world.Parallel intelligence and sim2real technologies extend the physical space to the virtual space and model the real world through virtual-real interac-tion.Therefore,the vehicle agent can gain knowledge and experience through the simulation and real-life environments.The core technology of PI is to make decisions through the interaction between the real and artificial driving systems and realize the management and control of the driving system using comparison,learning,and experimentation of the two sys-tems.Compared with sim2real methods,parallel intelligence deals with the relationship between simulated and real sce-narios from a higher technical level,solves complex modeling problems,and markedly reduces the difference between simulated and real scenarios.In the field of autonomous driving,PI has developed several branches,mainly including the parallel system,parallel learning,parallel driving,and parallel planning.Moreover,the theoretical system has been con-tinuously developed and has achieved remarkable results in numerous fields,such as transportation,medical treatment,manufacturing,and control.Subsequently,some autonomous driving simulators,such as AirSim and CARLA,are pre-sented in this paper.Simulators for autonomous driving generally aim to minimize the mismatch between real and simulated setups by providing training data and experience,thus enabling the deployment of vehicle agents into the real world.Finally,existing challenges and future perspectives in sim2real and PI methods are summarized.With the continuous development of simulation-to-reality technologies,additional breakthroughs and progress in autonomous driving will be achieved in the future.
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