Decision-Making in Driver-Automation Shared Control:A Review and Perspectives

Shared control schemes allow a human driver to work with an automated driving agent in driver-vehicle systems while retaining the driver’s abilities to control.The human driver,as an essential agent in the driver-vehicle shared control systems,should be precisely modeled regarding their cognitive processes,control strategies,and decision-making processes.The interactive strategy design between drivers and automated driving agents brings an excellent challenge for human-centric driver assistance systems due to the inherent characteristics of humans.Many open-ended questions arise,such as what proper role of human drivers should act in a shared control scheme?How to make an intelligent decision capable of balancing the benefits of agents in shared control systems?Due to the advent of these attentions and questions,it is desirable to present a survey on the decision making between human drivers and highly automated vehicles,to understand their architectures,human driver modeling,and interaction strategies under the driver-vehicle shared schemes.Finally,we give a further discussion on the key future challenges and opportunities.They are likely to shape new potential research directions.

Generation and Selection of Driver-Behavior-Based Transferable MotionPrimitives

To integrate driver experience and heterogeneous vehicle platform characteristics in a motion-planning algorithm,based on the driver-behavior-based transferable motion primitives(MPs), a general motion-planning framework for offline generation and online selection of MPs is proposed. Optimal control theory is applied to solve the boundary value problems in the process of generating MPs, where the driver behaviors and the vehicle motion characteristics are integrated into the optimization in the form of constraints. Moreover, a layered, unequal-weighted MP selection framework is proposed that utilizes a combination of environmental constraints, nonholonomic vehicle constraints,trajectory smoothness, and collision risk as the single-step extension evaluation index. The library of MPs generated offline demonstrates that the proposed generation method realizes the effective expansion of MP types and achieves diverse generation of MPs with various velocity attributes and platform types. We also present how the MP selection algorithm utilizes a unique MP library to achieve online extension of MP sequences. The results show that the proposed motion-planning framework can not only improve the efficiency and rationality of the algorithm based on driving experience but can also transfer between heterogeneous vehicle platforms and highlight the unique motion characteristics of the platform.

Efficient production of ligand-free microscintillators at gram-scale for high-resolution X-ray luminescence imaging

The efficient production of high-quality scintillators with long radioluminescence afterglow is crucial for high-performance X-ray luminescence extension imaging.However,scaling-up the synthesis of ligand-free scintillators to fabricate large-area X-ray imaging screens for industrial applications remains a challenge.In this study,we report an efficient method to synthesize ligand-free,lanthanide-doped microscintillators by a one-pot reaction via the concentrated hydrothermal method.The as-synthesized microscintillators exhibit prolonged persistent radioluminescence for up to 30 days after X-ray exposure and remain high stability in air or water for more than 18 months without deterioration.Monte Carlo simulations indicate that the size effect is responsible for the excellent afterglow performance of the microscintillators.We employ these high-quality lanthanide-doped microscintillators to fabricate a large-area X-ray imaging detector using a blade-coating method,a spatial resolution of 24.9 lp/mm for X-ray imaging.Our study offers a solution for scaling-up the synthesis of low-cost microscintillators for practical applications.

Continual driver behaviour learning for connected vehicles and intelligent transportation systems: Framework, survey and challenges

Modelling,predicting and analysing driver behaviours are essential to advanced driver assistance systems(ADAS)and the comprehensive understanding of complex driving scenarios.Recently,with the development of deep learning(DL),numerous driver behaviour learning(DBL)methods have been proposed and applied in connected vehicles(CV)and intelligent transportation systems(ITS).This study provides a review of DBL,which mainly focuses on typical applications in CV and ITS.First,a comprehensive review of the state-of-the-art DBL is presented.Next,Given the constantly changing nature of real driving scenarios,most existing learning-based models may suffer from the so-called“catastrophic forgetting,”which refers to their inability to perform well in previously learned scenarios after acquiring new ones.As a solution to the aforementioned issue,this paper presents a framework for continual driver behaviour learning(CDBL)by leveraging continual learning technology.The proposed CDBL framework is demonstrated to outperform existing methods in behaviour prediction through a case study.Finally,future works,potential challenges and emerging trends in this area are highlighted.

Autonomous Overtaking for Intelligent Vehicles Considering Social Preference Based on Hierarchical Reinforcement Learning

As intelligent vehicles usually have complex overtaking process,a safe and efficient automated overtaking system(AOS)is vital to avoid accidents caused by wrong operation of drivers.Existing AOSs rarely consider longitudinal reactions of the overtaken vehicle(OV)during overtaking.This paper proposed a novel AOS based on hierarchical reinforcement learning,where the longitudinal reaction is given by a data-driven social preference estimation.This AOS incorporates two modules that can function in different overtaking phases.The first module based on semi-Markov decision process and motion primitives is built for motion planning and control.The second module based on Markov decision process is designed to enable vehicles to make proper decisions according to the social preference of OV.Based on realistic overtaking data,the proposed AOS and its modules are verified experimentally.The results of the tests show that the proposed AOS can realize safe and effective overtaking in scenes built by realistic data,and has the ability to flexibly adjust lateral driving behavior and lane changing position when the OVs have different social preferences.

Off-road testing scenario design and library generation for intelligent vehicles

To realize the widespread application and continuous functional development of intelligent vehicles,test and evaluation of vehicle's functionality and Safety Performance in complex off-road scenarios are fundamental.Since traditional distance-based road tests cannot meet the evolving test requirements,a method to design the function-based off-road testing scenario library for intelligent vehicles(IV)is proposed in this paper.The testing scenario library is defined as a critical set of scenarios that can be used for IV tests.First,for the complex and diverse off-road scenarios,a hierarchical,structural model of the test scenario is built.Then,the critical test scenarios are selected adaptively according to the vehicle model to be tested.Next,those parameters representing the challenging test scenarios are selected.The selected parameters need to fit the natural distribution probability of scenarios.The critical test-scenario library is built combing these parameters with the structural model.Finally,the test scenarios that are most approximate to the natural driving scenario are determined with importance sampling theory.The test-scenario library built with this method can provide more critical test scenarios,and is widely applicable despite different vehicle models.Verified by simulation in the off-road interaction scenarios,test would be accelerated significantly with this method,about 800 times faster than testing in the natural road environment.

A hybrid motion planning framework for autonomous driving in mixedtraffic flow

As a core part of an autonomous driving system,motion planning plays an important role in safe driving.However,traditional model-and rule-based methods lack the ability to learn interactively with the environment,and learning-based methods still have problems in terms of reliability.To overcome these problems,a hybrid motion planning framework(HMPF)is proposed to improve the performance of motion planning,which is composed of learning-based behavior planning and optimization-based trajectory planning.The behavior planning module adopts a deep reinforcement learning(DRL)algorithm,which can learn from the interaction between the ego vehicle(EV)and other human-driven vehicles(HDVs),and generate behavior decision commands based on environmental perception information.In particular,the intelligent driver model(IDM)calibrated based on real driving data is used to drive HDVs to imitate human driving behavior and interactive response,so as to simulate the bidirectional interaction between EV and HDVs.Meanwhile,trajectory planning module adopts the optimization method based on road Frenet coordinates,which can generate safe and comfortable desired trajectory while reducing the solution dimension of the problem.In addition,trajectory planning also exists as a safety hard constraint of behavior planning to ensure the feasibility of decision instruction.The experimental results demonstrate the effectiveness and feasibility of the proposed HMPF for autonomous driving motion planning in urban mixed traffic flow scenarios.