Safe Motion Planning and Control Framework for Automated Vehicles with Zonotopic TRMPC

Model mismatches can cause multi-dimensional uncertainties for the receding horizon control strategies of automated vehicles(AVs).The uncertainties may lead to potentially hazardous behaviors when the AV tracks ideal trajectories that are individually optimized by the AV's planning layer.To address this issue,this study proposes a safe motion planning and control(SMPAC)framework for AVs.For the control layer,a dynamic model including multi-dimensional uncertainties is established.A zonotopic tube-based robust model predictive control scheme is proposed to constrain the uncertain system in a bounded minimum robust positive invariant set.A flexible tube with varying cross-sections is constructed to reduce the controller conservatism.For the planning layer,a concept of safety sets,representing the geometric boundaries of the ego vehicle and obstacles under uncertainties,is proposed.The safety sets provide the basis for the subsequent evaluation and ranking of the generated trajectories.An efficient collision avoidance algorithm decides the desired trajectory through the intersection detection of the safety sets between the ego vehicle and obstacles.A numerical simulation and hardware-in-the-loop experiment validate the effectiveness and real-time performance of the SMPAC.The result of two driving scenarios indicates that the SMPAC can guarantee the safety of automated driving under multi-dimensional uncertainties.

A Review of Testing Object‑Based Environment Perception for Safe Automated Driving

Safety assurance of automated driving systems must consider uncertain environment perception.This paper reviews litera-ture addressing how perception testing is realized as part of safety assurance.The paper focuses on testing for verification and validation purposes at the interface between perception and planning,and structures the analysis along the three axes(1)test criteria and metrics,(2)test scenarios,and(3)reference data.Furthermore,the analyzed literature includes related safety standards,safety-independent perception algorithm benchmarking,and sensor modeling.It is found that the realiza-tion of safety-oriented perception testing remains an open issue since challenges concerning the three testing axes and their interdependencies currently do not appear to be sufficiently solved.

Trajectory Planning for Automated Driving Based on Ordinal Optimization

This paper proposes an approach based on Ordinal Optimization（OO） to solve trajectory planning for automated driving. As most planning approaches based on candidate curves optimize the trajectory curve and the velocity profile separately, this paper formulates the problem as an unified Non-Linear Programming（NLP） model,optimizing the trajectory curve and the acceleration profile（acceleration is the derivative of velocity） simultaneously.Then a hybrid optimization algorithm named OODE, developed by combining the idea of OO and Differential Evolution（DE）, is proposed to solve the NLP model. With the acceleration profile optimized ＂roughly＂, OODE computes and compares ＂rough＂（biased but computationally-easier） curve evaluations to select the best curve from candidates, so that a good enough curve can be obtained very efficiently. Then the acceleration profile is optimized again ＂accurately＂ with the selected curve. Simulation results show that good enough solutions are ensured with a high probability and our method is capable of working in real time.

Personality Openness Predicts Driver Trust in Automated Driving

Maintaining an appropriate level of trust in automated driving(AD)is critical to safe driving.However,few studies have explored factors affecting trust in AD in general,and no study,as far as is known,has directly investigated whether driver personality influences driver trust in an AD system.The current study investigates the relation between driver personality and driver trust in AD,focusing on Level 2 AD.Participants were required to perform a period of AD in a driving simulator,during which their gaze and driving behavior were recorded,as well as their subjective trust scores after driving.In three distinct measures,a significant correlation between Openness and driver trust in the AD system is found:participants with higher Openness traits tend to have less trust in the AD system.No significant correlations between driver trust in AD and other personality traits are found.The findings suggest that driver personality has an impact on driver trust in AD.Theoretical and practical implications of this finding are discussed.

Simulator Coupled with Distributed Co-Simulation Protocol for Automated Driving Tests

To meet the challenges in software testing for automated vehicles,such as increasing system complexity and an infinite number of operating scenarios,new simulation methods must be developed.Closed-loop simulations for automated driving(AD)require highly complex simulation models for multiple controlled vehicles with their perception systems as well as their surrounding context.For the realization of such models,different simulation domains must be coupled with co-simulation.However,widely supported model integration standards such as functional mock-up interface(FMI)lack native support for distributed platforms,which is a key feature for AD due to the computational intensity and platform exclusivity of certain models.The newer FMI companion standard distributed co-simulation protocol(DCP)introduces platform coupling but must still be used in conjunction with AD co-simulations.As part of an assessment framework for AD,this paper presents a DCP compliant implementation of an interoperable interface between a 3D environment and vehicle simulator and a co-simulation platform.A universal Python wrapper is implemented and connected to the simulator to allow its control as a DCP slave.A C-code-based interface enables the co-simulation platform to act as a DCP master and to realize cross-platform data exchange and time synchronization of the environment simulation with other integrated models.A model-in-the-loop use case is performed with the traffic simulator CARLA running on a Linux machine connected to the co-simulation master xMOD on a Windows computer via DCP.Several virtual vehicles are successfully controlled by cooperative adaptive cruise controllers executed outside of CARLA.The standard compliance of the implementation is verified by exemplary connection to prototypic DCP solutions from 3rd party vendors.This exemplary application demonstrates the benefits of DCP compliant tool coupling for AD simulation with increased tool interoperability,reuse potential,and performance.

Developing High-Precision Maps for Automated Driving in China:Legal Obstacles and the Way to Overcome Them

A high-precision map(HPM)is the key infrastructure to realizing the function of automated driving(AD)and ensuring its safety.However,the current laws and regulations on HPMs in China can lead to serious legal compliance problems.Thus,proper measures should be taken to remove these barriers.Starting with a complete view of the current legal obstacles to HPMs in China,this study first explains why these legal obstacles exist and the types of legal interests they are trying to protect.It then analyzes whether new technology could be used as an alternative to resolve these concerns.Factors such as national security,AD industry needs,and personal data protection,as well as the flexibility of applying technology,are discussed and analyzed hierarchically for this purpose.This study proposes that China should adhere to national security and AD industry development,pass new technical regulations that redefine the scope of national security regarding geographic information in the field of HPMs,and establish a national platform under the guidance and monitoring of the government to integrate scattered resources and promote the development of HPMs via crowdsourcing.Regarding the legal obstacles with higher technical plasticity,priority should be given to technical solutions such as“available but invisible”technology.Compared with the previous research,this study reveals the current legal barriers in China that have different levels of relevance to national security and different technical plasticity.It also proposes original measures to remove them,such as coordinating national security with the development of the AD industry,reshaping the boundary of national security and industrial interests,and giving priority to technical solutions for legal barriers that have strong technical plasticity.

Performance Evaluation Method for Automated Driving System in Logical Scenario

With the continuous improvement of automated driving technology,how to evaluate the performance of an automated driving system is attracting more and more attention.Meanwhile,with the creation of scenario-based test methods,the traditional evaluation index based on a single test can no longer meet the requirements of high-level safety verification for automated driving system,and the performance evaluation of such a system in logical scenarios will be the mainstream.Based on the scenario-based test method and Turing test theory,a performance evaluation method for an automated driving system in the whole parameter space of a logical scenario is proposed.The logical scenario parameter space is partitioned according to the risk degree of concrete scenario,and the evaluation process in different zones are determined.Subsequently,the anthropo-morphic index in the safe zone and the collision-avoidance index in the danger zone are defined by comparing test results of human driving and ideal vehicle motion.Taking front vehicle low-speed and cut-out scenarios as examples,two automated driving algorithms are tested in the virtual environment,and the test results are evaluated both by the proposed method and by human observation.The results show that the results of the proposed method are consistent with the subjective feelings of humans;additionally,it can be applied to scenario-based tests and the verification process of an automated driving system.

Slow driving control of tracked vehicles with automated mechanical transmission based on fuzzy logic

In order to move tracked vehicles at an extremely slowspeed with automated mechanical transmission（ AMT）,slowdriving function was added in the original system. The principle and requirement of slowdriving function were analyzed. Based on analysis of slow driving characteristic,identification of slowdriving condition and fuzzy control algorithm,a control strategy of the clutch was designed. In order to realize slowdriving,the clutch was controlled in a slipping mode as manual driving. The vehicle speed was increased to a required speed and kept in a small range by engaging or disengaging the clutch to the approximate half engagement point. Based on the control strategy,a control software was designed and tested on a tracked vehicle with AMT. The test results showthat the control of the clutch with the slowdriving function was smoother than that with original systemand the vehicle speed was slower and steadier.

Parallel Driving in CPSS:A Unified Approach for Transport Automation and Vehicle Intelligence

The emerging development of connected and automated vehicles imposes a significant challenge on current vehicle control and transportation systems. This paper proposes a novel unified approach, Parallel Driving, a cloud-based cyberphysical-social systems(CPSS) framework aiming at synergizing connected automated driving. This study first introduces the CPSS and ACP-based intelligent machine systems. Then the parallel driving is proposed in the cyber-physical-social space,considering interactions among vehicles, human drivers, and information. Within the framework, parallel testing, parallel learning and parallel reinforcement learning are developed and concisely reviewed. Development on intelligent horizon(iHorizon)and its applications are also presented towards parallel horizon.The proposed parallel driving offers an ample solution for achieving a smooth, safe and efficient cooperation among connected automated vehicles with different levels of automation in future road transportation systems.

A New Speed Limit Recognition Methodology Based on Ensemble Learning:Hardware Validation

Advanced DriverAssistance Systems(ADAS)technologies can assist drivers or be part of automatic driving systems to support the driving process and improve the level of safety and comfort on the road.Traffic Sign Recognition System(TSRS)is one of themost important components ofADAS.Among the challengeswith TSRS is being able to recognize road signs with the highest accuracy and the shortest processing time.Accordingly,this paper introduces a new real time methodology recognizing Speed Limit Signs based on a trio of developed modules.Firstly,the Speed Limit Detection(SLD)module uses the Haar Cascade technique to generate a new SL detector in order to localize SL signs within captured frames.Secondly,the Speed Limit Classification(SLC)module,featuring machine learning classifiers alongside a newly developed model called DeepSL,harnesses the power of a CNN architecture to extract intricate features from speed limit sign images,ensuring efficient and precise recognition.In addition,a new Speed Limit Classifiers Fusion(SLCF)module has been developed by combining trained ML classifiers and the DeepSL model by using the Dempster-Shafer theory of belief functions and ensemble learning’s voting technique.Through rigorous software and hardware validation processes,the proposedmethodology has achieved highly significant F1 scores of 99.98%and 99.96%for DS theory and the votingmethod,respectively.Furthermore,a prototype encompassing all components demonstrates outstanding reliability and efficacy,with processing times of 150 ms for the Raspberry Pi board and 81.5 ms for the Nano Jetson board,marking a significant advancement in TSRS technology.

Methodical Approach to Integrate Human Movement Diversity in Real-Time into a Virtual Test Field for Highly Automated Vehicle Systems

Recently, virtual realities and simulations play important roles in the development of automated driving functionalities. By an appropriate abstraction, they help to design, investigate and communicate real traffic scenario complexity. Especially, for edge cases investigations of interactions between vulnerable road users (VRU) and highly automated driving functions, valid virtual models are essential for the quality of results. The aim of this study is to measure, process and integrate real human movement behaviour into a virtual test environment for highly automated vehicle functionalities. The overall system consists of a georeferenced virtual city model and a vehicle dynamics model, including probabilistic sensor descriptions. By motion capture hardware, real humanoid behaviour is applied to a virtual human avatar in the test environment. Through retargeting methods, which enable the independency of avatar and person under test (PuT) dimensions, the virtual avatar diversity is increased. To verify the biomechanical behaviour of the virtual avatars, a qualitative study is performed, which funds on a representative movement sequence. The results confirm the functionality of the used methodology and enable PuT independence control of the virtual avatars in real-time.

Safe, Efficient and Socially‑Compatible Decision of Automated Vehicles: A Case Study of Unsignalized Intersection Driving

Safe and smooth interaction between other vehicles is one of the ultimate goals of driving automation.However,recent reports of demonstrative deployments of automated vehicles(AVs)indicate that AVs are still difficult to meet the expecta-tion of other interacting drivers,which leads to several AV accidents involving human-driven vehicles(HVs)without the understanding about the dynamic interaction process.By investigating 4300 video clips of traffic accidents,it is found that the limited dynamic visual field of drivers is one leading factor in inter-vehicle interaction accidents.A game-theoretic decision algorithm considering social compatibility is proposed to handle the interaction with a human-driven truck at an unsignalized intersection.Starting from a probabilistic model for the visual field characteristics of truck drivers,social fit-ness and reciprocal altruism in the decision are incorporated in the game payoff design.Human-in-the-loop experiments are carried out,in which 24 subjects are invited to drive and interact with AVs deployed with the proposed algorithm and two comparison algorithms.Totally,207 cases of intersection interactions are obtained and analyzed,which shows that the proposed decision-making algorithm can improve both safety and time efficiency,and make AV decisions more in line with the expectation of interacting human drivers.These findings can help inform the design of automated driving decision algorithms,to ensure that AVs can be safely and efficiently integrated into the human-dominated traffic.

Cloud Control System Architectures,Technologies and Applications on Intelligent and Connected Vehicles:a Review

The electrification of vehicle helps to improve its operation efficiency and safety.Due to fast development of network,sensors,as well as computing technology,it becomes realizable to have vehicles driving autonomously.To achieve autonomous driving,several steps,including environment perception,path-planning,and dynamic control,need to be done.However,vehicles equipped with on-board sensors still have limitations in acquiring necessary environmental data for optimal driving decisions.Intelligent and connected vehicles(ICV)cloud control system(CCS)has been introduced as a new concept as it is a potentially synthetic solution for high level automated driving to improve safety and optimize traffic flow in intelligent transportation.This paper systematically investigated the concept of cloud control system from cloud related applications on ICVs,and cloud control system architecture design,as well as its core technologies development.Based on the analysis,the challenges and suggestions on cloud control system development have been addressed.

Deep Reinforcement Learning Based Decision‑Making Strategy of Autonomous Vehicle in Highway Uncertain Driving Environments

Uncertain environment on multi-lane highway,e.g.,the stochastic lane-change maneuver of surrounding vehicles,is a big challenge for achieving safe automated highway driving.To improve the driving safety,a heuristic reinforcement learning decision-making framework with integrated risk assessment is proposed.First,the framework includes a long short-term memory model to predict the trajectory of surrounding vehicles and a future integrated risk assessment model to estimate the possible driving risk.Second,a heuristic decaying state entropy deep reinforcement learning algorithm is introduced to address the exploration and exploitation dilemma of reinforcement learning.Finally,the framework also includes a rule-based vehicle decision model for interaction decision problems with surrounding vehicles.The proposed framework is validated in both low-density and high-density traffic scenarios.The results show that the traffic efficiency and vehicle safety are both improved compared to the common dueling double deep Q-Network method and rule-based method.

European Research Project’s Contributions to a Safer Automated Road Traffic

Automated driving is poised to become a pivotal technology in the future automotive transportation.However,it is evident that the implementation of automated driving presents significant technical challenges.To accelerate the development and deployment of automated driving the European Commission initiated the research project L3Pilot in 2017.With a budget of 65 million Euros and the involvement of 13 car manufacturers,L3Pilot stands as the largest European project on automated driving(AD).This paper serves as a comprehensive account of BMW’s main activities in the L3Pilot project that ended in 2021.The research questions addressed in this project are related to the following topics:what are the guidelines for the development of AD?How do potential customers interact with AD?And what is the safety impact assessment of AD?The paper presents the findings related to all three research questions to contribute to the further development of automated driving.For this purpose together with other partners the Code of Practice of AD was defined as a guideline for the development of future AD systems.Related to the second question,BMW conducted tests with AD systems on motorways and in parking scenarios,with over 100 test subjects experiencing AD.The studies provide input and considerations for future AD systems.Finally,in the safety impact assessment,BMW investigated with other project partners the potential safety benefits of AD through simulation.The results show a potential to improve road safety.In conclusion,the exploration of all three research questions has led to a deeper understanding of SAE Level 3 AD.

Kinematics-aware multigraph attention network with residual learning for heterogeneous trajectory prediction

Trajectory prediction for heterogeneous traffic agents plays a crucial role in ensuring the safety and efficiency of automated driving in highly interactive traffic environments.Numerous studies in this area have focused on physicsbased approaches because they can clearly interpret the dynamic evolution of trajectories.However,physics-based methods often suffer from limited accuracy.Recent learning-based methods have demonstrated better performance,but they cannot be fully trusted due to the insufficient incorporation of physical constraints.To mitigate the limitations of purely physics-based and learning-based approaches,this study proposes a kinematics-aware multigraph attention network(KAMGAT)that incorporates physics models into a deep learning framework to improve the learning process of neural networks.Besides,we propose a residual prediction module to further refine the trajectory predictions and address the limitations arising from simplified assumptions in kinematic models.We evaluate our proposed model through experiments on two challenging trajectory datasets,namely,ApolloScape and NGSIM.Our findings from the experiments demonstrate that our model outperforms various kinematics-agnostic models with respect to prediction accuracy and learning efficiency.

Development of deep-learning-based autonomous agents for low-speed maneuvering in Unity

This study provides a systematic analysis of the resource-consuming training of deep reinforcement-learning (DRL) agents for simulated low-speed automated driving (AD). In Unity, this study established two case studies: garage parking and navigating an obstacle-dense area. Our analysis involves training a path-planning agent with real-time-only sensor information. This study addresses research questions insufficiently covered in the literature, exploring curriculum learning (CL), agent generalization (knowledge transfer), computation distribution (CPU vs. GPU), and mapless navigation. CL proved necessary for the garage scenario and beneficial for obstacle avoidance. It involved adjustments at different stages, including terminal conditions, environment complexity, and reward function hyperparameters, guided by their evolution in multiple training attempts. Fine-tuning the simulation tick and decision period parameters was crucial for effective training. The abstraction of high-level concepts (e.g., obstacle avoidance) necessitates training the agent in sufficiently complex environments in terms of the number of obstacles. While blogs and forums discuss training machine learning models in Unity, a lack of scientific articles on DRL agents for AD persists. However, since agent development requires considerable training time and difficult procedures, there is a growing need to support such research through scientific means. In addition to our findings, we contribute to the R&D community by providing our environment with open sources.

Human Performance in Critical Scenarios as a Benchmark for Highly Automated Vehicles

Before highly automated vehicles(HAVs)become part of everyday traffic,their safety has to be proven.The use of human performance as a benchmark represents a promising approach,but appropriate methods to quantify and compare human and HAV performance are rare.By adapting the method of constant stimuli,a scenario-based approach to quantify the limit of(human)performance is developed.The method is applied to a driving simulator study,in which participants are repeatedly confronted with a cut-in manoeuvre on a highway.By systematically manipulating the criticality of the manoeuvre in terms of time to collision,humans’collision avoidance performance is measured.The limit of human performance is then identified by means of logistic regression.The calculated regression curve and its inflection point can be used for direct comparison of human and HAV performance.Accordingly,the presented approach represents one means by which HAVs’safety performance could be proven.

Toward Shared Control Between Automated Vehicles and Users

Technological developments in the domain of vehicle automation are targeted toward driver-less,or driver-out-of-the-loop driving.The main societal motivation for this ambition is that the majority of(fatal)accidents with manually driven vehicles are due to human error.However,when interacting with technology,users often experience the need to customize the technology to their personal preferences.This paper considers how this might apply to vehicle automation,by a conceptual analysis of relevant use cases.The analysis proceeds by comparing how handling of relevant situations is likely to differ between manual driving and automated driving.The results of the analysis indicate that full out-of-the-loop automated driving may not be acceptable to users of the technology.It is concluded that a technology that allows shared control between the vehicle and the user should be pursued.Furthermore,implications of this view are explored for the concrete temporal dynamics of shared control,and general characteristics of human machine interface that support shared control are proposed.Finally,implications of the proposed view and directions for further research are discussed.

Potential impact of autonomous vehicles in mixed traffic from simulation using real traffic flow

This work focuses on the potential impacts of the autonomous vehicles in a mixed traffic condition represented in traffic simulator Simulation of Urban MObility(SUMO)with real traffic flow.Specifically,real traffic flow and speed data collected in 2002 and 2019 in Gothenburg were used to simulate daily flow variation in SUMO.In order to predict the most likely drawbacks during the transition from a traffic consisting only manually driven vehicles to a traffic consisting only fully-autonomous vehicles,this study focuses on mixed traffic with different percentages of autonomous and manually driven vehicles.To realize this aim,several parameters of the car following and lane change models of autonomous vehicles are investigated in this paper.Along with the fundamental diagram,the number of lane changes and the number of conflicts are analyzed and studied as measures for improving road safety and efficiency.The study highlights that the autonomous vehicles’features that improve safety and efficiency in 100%autonomous and mixed traffic are different,and the ability of autonomous vehicles to switch between mixed and autonomous driving styles,and vice versa depending on the scenario,is necessary.