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.

Resilient and Safe Platooning Control of Connected Automated Vehicles Against Intermittent Denial-of-Service Attacks

Connected automated vehicles(CAVs)serve as a promising enabler for future intelligent transportation systems because of their capabilities in improving traffic efficiency and driving safety,and reducing fuel consumption and vehicle emissions.A fundamental issue in CAVs is platooning control that empowers a convoy of CAVs to be cooperatively maneuvered with desired longitudinal spacings and identical velocities on roads.This paper addresses the issue of resilient and safe platooning control of CAVs subject to intermittent denial-of-service(DoS)attacks that disrupt vehicle-to-vehicle communications.First,a heterogeneous and uncertain vehicle longitudinal dynamic model is presented to accommodate a variety of uncertainties,including diverse vehicle masses and engine inertial delays,unknown and nonlinear resistance forces,and a dynamic platoon leader.Then,a resilient and safe distributed longitudinal platooning control law is constructed with an aim to preserve simultaneous individual vehicle stability,attack resilience,platoon safety and scalability.Furthermore,a numerically efficient offline design algorithm for determining the desired platoon control law is developed,under which the platoon resilience against DoS attacks can be maximized but the anticipated stability,safety and scalability requirements remain preserved.Finally,extensive numerical experiments are provided to substantiate the efficacy of the proposed platooning method.

Traffic flow of connected and automated vehicles at lane drop on two-lane highway: An optimization-based control algorithm versus a heuristic rules-based algorithm

This paper investigates traffic flow of connected and automated vehicles at lane drop on two-lane highway. We evaluate and compare performance of an optimization-based control algorithm(OCA) with that of a heuristic rules-based algorithm(HRA). In the OCA, the average speed of each vehicle is maximized. In the HRA, virtual vehicle and restriction of the command acceleration caused by the virtual vehicle are introduced. It is found that(i) capacity under the HRA(denoted as C_(H)) is smaller than capacity under the OCA;(ii) the travel delay is always smaller under the OCA, but driving is always much more comfortable under the HRA;(iii) when the inflow rate is smaller than C_(H), the HRA outperforms the OCA with respect to the fuel consumption and the monetary cost;(iv) when the inflow rate is larger than C_(H), the HRA initially performs better with respect to the fuel consumption and the monetary cost, but the OCA would become better after certain time. The spatiotemporal pattern and speed profile of traffic flow are presented, which explains the reason underlying the different performance. The study is expected to help for better understanding of the two different types of algorithm.

Integrated Sensing and Communication Enabled Multiple Beamwidth and Power Allocation for Connected Automated Vehicles

Connected autonomous vehicles(CAVs)are a promising paradigm for implementing intelligent transportation systems.However,in CAVs scenarios,the sensing blind areas cause serious safety hazards.Existing vehicle-to-vehicle(V2V)technology is difficult to break through the sensing blind area and ensure reliable sensing information.To overcome these problems,considering infrastructures as a means to extend the sensing range is feasible based on the integrated sensing and communication(ISAC)technology.The mmWave base station(mmBS)transmits multiple beams consisting of communication beams and sensing beams.The sensing beams are responsible for sensing objects within the CAVs blind area,while the communication beams are responsible for transmitting the sensed information to the CAVs.To reduce the impact of inter-beam interference,a joint multiple beamwidth and power allocation(JMBPA)algorithm is proposed.By maximizing the communication transmission rate under the sensing constraints.The proposed non-convex optimization problem is transformed into a standard difference of two convex functions(D.C.)problem.Finally,the superiority of the lutions.The average transmission rate of communication beams remains over 3.4 Gbps,showcasing a significant improvement compared to other algorithms.Moreover,the satisfaction of sensing services remains steady.

Efficient control of connected and automated vehicles on a two-lane highway with a moving bottleneck

This paper investigates the traffic flow of connected and automated vehicles(CAVs)inducing by a moving bottleneck on a two-lane highway.A heuristic rules-based algorithm(HRA)has been used to control the traffic flow upstream of the moving bottleneck.In the HRA,some CAVs in the control zone are mapped onto the neighboring lane as virtual ones.To improve the driving comfort,the command acceleration caused by virtual vehicle is restricted.Comparing with the benchmark in which the CAVs change lane as soon as the lane changing condition is met,the HRA significantly improves the traffic flow:the overtaking throughput as well as the outflow rate increases,the travel delay and the fuel consumption decrease,the comfort level could also be improved.

Stability of connected and automated vehicles platoon considering communications failures

As a form of a future traffic system,a connected and automated vehicle(CAV)platoon is a typical nonlinear physical system.CAVs can communicate with each other and exchange information.However,communication failures can change the platoon system status.To characterize this change,a dynamic topology-based car-following model and its generalized form are proposed in this work.Then,a stability analysis method is explored.Finally,taking the dynamic cooperative intelligent driver model(DC-IDM)for example,a series of numerical simulations is conducted to analyze the platoon stability in different communication topology scenarios.The results show that the communication failures reduce the stability,but information from vehicles that are farther ahead and the use of a larger desired time headway can improve stability.Moreover,the critical ratio of communication failures required to ensure stability for different driving parameters is studied in this work.

Dynamic Event-Triggered Scheduling and Platooning Control Co-Design for Automated Vehicles Over Vehicular Ad-Hoc Networks

This paper deals with the co-design problem of event-triggered communication scheduling and platooning control over vehicular ad-hoc networks(VANETs)subject to finite communication resource.First,a unified model is presented to describe the coordinated platoon behavior of leader-follower vehicles in the simultaneous presence of unknown external disturbances and an unknown leader control input.Under such a platoon model,the central aim is to achieve robust platoon formation tracking with desired inter-vehicle spacing and same velocities and accelerations guided by the leader,while attaining improved communication efficiency.Toward this aim,a novel bandwidth-aware dynamic event-triggered scheduling mechanism is developed.One salient feature of the scheduling mechanism is that the threshold parameter in the triggering law is dynamically adjusted over time based on both vehicular state variations and bandwidth status.Then,a sufficient condition for platoon control system stability and performance analysis as well as a co-design criterion of the admissible event-triggered platooning control law and the desired scheduling mechanism are derived.Finally,simulation results are provided to substantiate the effectiveness and merits of the proposed co-design approach for guaranteeing a trade-off between robust platooning control performance and communication efficiency.

Comparative Study of Trajectory Tracking Control for Automated Vehicles via Model Predictive Control and Robust H-infinity State Feedback Control

A comparative study of model predictive control(MPC)schemes and robust Hstate feedback control(RSC)method for trajectory tracking is proposed in this paper.The main objective of this paper is to compare MPC and RSC controllers’performance in tracking predefined trajectory under different scenarios.MPC controller is designed based on the simple longitudinal-yaw-lateral motions of a single-track vehicle with a linear tire,which is an approximation of the more realistic model of a vehicle with double-track motion with a non-linear tire mode.RSC is designed on the basis of the same method as adopted for the MPC controller to achieve a fair comparison.Then,three test cases are built in CarSim-Simulink joint platform.Specifically,the verification test is used to test the tracking accuracy of MPC and RSC controller under well road conditions.Besides,the double lane change test with low road adhesion is designed to find the maximum velocity that both controllers can carry out while guaranteeing stability.Furthermore,an extreme curve test is built where the road adhesion changes suddenly,in order to test the performance of both controllers under extreme conditions.Finally,the advantages and disadvantages of MPC and RSC under different scenarios are also discussed.

On String Stable Control of Platoon of Automated Vehicles with Predecessor-successor Information Framework

For the constant distance spacing policy,the existing researches of the string stability focus on the single-predecessor information framework（SPIF） and predecessor-successor information framework（PSIF）.The research results demonstrated that the string stability could not be guaranteed with the SPIF,and then the PSIF was proposed to resolve this string instability.But the issue,whether the string stability can be guaranteed when applying the PSIF,is still controversial.Meanwhile,most of the previous researches on the string stability were conducted without consideration of the parasitic time delays and lags.In this paper,the practical longitudinal vehicle dynamics model is built with consideration of the parasitic time delays and lags existing in the actuators,sensors or the communication systems.Secondly,the detailed theoretical analysis of string stability in frequency domain is conducted to demonstrate that the classical linear control laws can not ensure the string stability when applying both the symmetrical PSIF（SPSIF） and asymmetrical PSIF（APSIF）.Thirdly,a control law,which adds the position and velocity information of the leading vehicle,is proposed to guarantee string stability for small/medium platoon,and the other control law,which adds the acceleration information of the controlled vehicle,is proposed to guarantee string stability for large platoon as well as small/medium platoon.Finally,the comparative simulation is conducted to confirm the conducted analysis and the proposed control laws.The conducted research completes the means to analyze the string stability in frequency domain,provides the parameters＇ reference for the design and implementation of the practical automatic following controllers,and improves the reliability and stability of the platoon of automatic vehicles.

Continuum modeling of freeway traffic flows:State-of-the-art,challenges and future directions in the era of connected and automated vehicles

Connected and automated vehicles(CAVs)are expected to reshape traffic flow dynamics and present new challenges and opportunities for traffic flow modeling.While numerous studies have proposed optimal modeling and control strategies for CAVs with various objectives(e.g.,traffic efficiency and safety),there are uncertainties about the flow dynamics of CAVs in real-world traffic.The uncertainties are especially amplified for mixed traffic flows,consisting of CAVs and human-driven vehicles,where the implications can be significant from the continuum-modeling perspective,which aims to capture macroscopic traffic flow dynamics based on hyperbolic systems of partial differential equations.This paper aims to highlight and discuss some essential problems in continuum modeling of real-world freeway traffic flows in the era of CAVs.We first provide a select review of some existing continuum models for conventional human-driven traffic as well as the recent attempts for incorporating CAVs into the continuum-modeling framework.Wherever applicable,we provide new insights about the properties of existing models and revisit their implications for traffic flows of CAVs using recent empirical observations with CAVs and the previous discussions and debates in the literature.The paper then discusses some major problems inherent to continuum modeling of real-world(mixed)CAV traffic flows modeling by distinguishing between two major research directions:(a)modeling for explaining purposes,where making reproducible inferences about the physical aspects of macroscopic properties is of the primary interest,and(b)modeling for practical purposes,in which the focus is on the reliable predictions for operation and control.The paper proposes some potential solutions in each research direction and recommends some future research topics.

Deep reinforcement learning-based drift parking control of automated vehicles

Drift parking usually requires precise control of a vehicle by a professional driver,which can reflect the performance of the vehicle under critical conditions.The obstacles to implementing this action include the high coupling between the longitudinal and lateral states,the high precision required for the vehicle initial state when the drift is triggered,and the difficulty in determining the reference state variables in the drift process.A two-segment drift parking control system is proposed in this paper.In the approaching control segment,the vehicle achieves the drift-triggered vehicle speed and pose,which relies on a pathtracking algorithm based on linear time-varying model predictive control.In the drifting control segment,the deep reinforcement learning algorithm twin-delayed deep deterministic policy gradient is creatively introduced to the controller design.It solves the precise vehicle motion control problem under the condition of the rear wheels having locked brakes.Through various simulations,the superiority and robustness to different initial conditions and abrupt changes in the parking space are verified.The effectiveness of the proposed control system is verified by a ground test.

Integrated safety and security enhancement of connected automated vehicles using DHR architecture

Safety and security are interrelated and both essential for connected automated vehicles(CAVs).They are usually investigated independently,followed by standards ISO 26262 and ISO/SAE 21434,respectively.However,more functional safety and security fea-tures of in-vehicle components make existing safety mechanisms weaken security mechanisms and vice versa.This results in a dilemma that the safety-critical and security-critical in-vehicle components cannot be protected.In this paper,we propose a dynamic heterogeneous redundancy(DHR)architecture to enhance the safety and security of CAVs simultaneously.We first investigate the current status of integrated safety and security analysis and explore the relationship between safety and security.Then,we propose a new taxonomy of in-vehicle components based on safety and security features.Finally,a dynamic heterogeneous redun-dancy(DHR)architecture is proposed to guarantee integrated functional safety and cyber security of connected vehicles for the first time.A case study on an automated bus shows that DHR architecture can not only detect unknown failures and ensure functional safety but also detect unknown attacks to protect cyber security.Furthermore,we provide an in-depth analysis of quantification for CAVs performance using DHR architecture and identify chal-lenges and future research directions.Overall,integrated safety and security enhancement is an emerging research direction.

Application of Automated Guided Vehicles in Smart Automated Warehouse Systems:A Survey

Automated Guided Vehicles(AGVs)have been introduced into various applications,such as automated warehouse systems,flexible manufacturing systems,and container terminal systems.However,few publications have outlined problems in need of attention in AGV applications comprehensively.In this paper,several key issues and essential models are presented.First,the advantages and disadvantages of centralized and decentralized AGVs systems were compared;second,warehouse layout and operation optimization were introduced,including some omitted areas,such as AGVs fleet size and electrical energy management;third,AGVs scheduling algorithms in chessboardlike environments were analyzed;fourth,the classical route-planning algorithms for single AGV and multiple AGVs were presented,and some Artificial Intelligence(AI)-based decision-making algorithms were reviewed.Furthermore,a novel idea for accelerating route planning by combining Reinforcement Learning(RL)andDijkstra’s algorithm was presented,and a novel idea of the multi-AGV route-planning method of combining dynamic programming and Monte-Carlo tree search was proposed to reduce the energy cost of systems.

A greedy path planning algorithm based on pre-path-planning and real-time-conflict for multiple automated guided vehicles in large-scale outdoor scenarios

With the wide application of automated guided vehicles(AGVs) in large scale outdoor scenarios with complex terrain,the collaborative work of a large number of AGVs becomes the main trend.The effective multi-agent path finding(MAPF) algorithm is urgently needed to ensure the efficiency and realizability of the whole system. The complex terrain of outdoor scenarios is fully considered by using different values of passage cost to quantify different terrain types. The objective of the MAPF problem is to minimize the cost of passage while the Manhattan distance of paths and the time of passage are also evaluated for a comprehensive comparison. The pre-path-planning and real-time-conflict based greedy(PRG) algorithm is proposed as the solution. Simulation is conducted and the proposed PRG algorithm is compared with waiting-stop A^(*) and conflict based search(CBS) algorithms. Results show that the PRG algorithm outperforms the waiting-stop A^(*) algorithm in all three performance indicators,and it is more applicable than the CBS algorithm when a large number of AGVs are working collaboratively with frequent collisions.

Not in Control,but Liable?Attributing Human Responsibility for Fully Automated Vehicle Accidents

Human agency has become increasingly limited in complex systems with increasingly automated decision-making capabilities.For instance,human occupants are passengers and do not have direct vehicle control in fully automated cars(i.e.,driverless cars).An interesting question is whether users are responsible for the accidents of these cars.Normative ethical and legal analyses frequently argue that individuals should not bear responsibility for harm beyond their control.Here,we consider human judgment of responsibility for accidents involving fully automated cars through three studies with seven experiments(N=2668).We compared the responsibility attributed to the occupants in three conditions:an owner in his private fully automated car,a passenger in a driverless robotaxi,and a passenger in a conventional taxi,where none of these three occupants have direct vehicle control over the involved vehicles that cause identical pedestrian injury.In contrast to normative analyses,we show that the occupants of driverless cars(private cars and robotaxis)are attributed more responsibility than conventional taxi passengers.This dilemma is robust across different contexts(e.g.,participants from China vs the Republic of Korea,participants with first-vs third-person perspectives,and occupant presence vs absence).Furthermore,we observe that this is not due to the perception that these occupants have greater control over driving but because they are more expected to foresee the potential consequences of using driverless cars.Our findings suggest that when driverless vehicles(private cars and taxis)cause harm,their users may face more social pressure,which public discourse and legal regulations should manage appropriately.

On-ramp merging strategy for connected and automated vehicles based on complete information static game

Improper handling of vehicle on-ramp merging may hinder traffic flow and contribute to lower fuel economy,while also increasing the risk of collisions.Cooperative control for connected and automated vehicles(CAVs)has the potential to significantly reduce negative environmental impact while also improve driving safety and traffic efficiency.Therefore,in this paper,we focus on the scenario of CAVs on-ramp merging and propose a centralized control method.Merging sequence(MS)allocation and motion planning are two key issues in this process.To deal with these problems,we first propose an MS allocation method based on a complete information static game whereby the mixed-strategy Nash equilibrium is calculated for an individual vehicle to select its strategy.The on-ramp merging problem is then formulated as a bi-objective(total fuel consumption and total travel time)optimization problem,to which optimal control based on Pontryagin's minimum principle(PMP)is applied to solve the motion planning issue.To determine the proper parameters in the bi-objective optimization problem,a varying-scale grid search method is proposed to explore possible solutions at different scales.In this method,an improved quicksort algorithm is designed to search for the Pareto front,and the(approximately)unbiased Pareto solution for the bi-objective optimization problem is finally determined as the optimal solution.The proposed on-ramp merging strategy is validated via numerical simulation,and comparison with other strategies demonstrates its effectiveness in terms of fuel economy and traffic efficiency.

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.

Pro-social control of connected automated vehicles in mixed-autonomy multi-lane highway traffic

We propose pro-social control strategies for connected automated vehicles(CAVs)to mitigate jamming waves in mixed-autonomy multi-lane traffic,resulting from car-following dynamics of human-driven vehicles(HDVs).Different from existing studies,which focus mostly on ego vehicle objectives to control CAVs in an individualistic manner,we devise a pro-social control algorithm.The latter takes into account the objectives(i.e.,driving comfort and traffic efficiency)of both the ego vehicle and surrounding HDVs to improve smoothness of the entire observable traffic.Under a model predictive control(MPC)framework that uses acceleration and lane change sequences of CAVs as optimization variables,the problem of individualistic,altruistic,and pro-social control is formulated as a non-convex mixed-integer nonlinear program(MINLP)and relaxed to a convex quadratic program through converting the piece-wise-linear constraints due to the optimal velocity with relative velocity(OVRV)car-following model into linear constraints by introducing slack variables.Low-fidelity simulations using the OVRV model and high-fidelity simulations using PTV VISSIM simulator show that pro-social and altruistic control can provide significant performance gains over individualistic driving in terms of efficiency and comfort on both single-and multi-lane roads.

Accelerated testing for automated vehicles safety evaluation in cut-in scenarios based on importance sampling,genetic algorithm and simulation applications

Purpose–It would take billions of miles’field road testing to demonstrate that the safety of automated vehicle is statistically significantly higher than the safety of human driving because that the accident of vehicle is rare event.Design/methodology/approach–This paper proposes an accelerated testing method for automated vehicles safety evaluation based on improved importance sampling(IS)techniques.Taking the typical cut-in scenario as example,the proposed method extracts the critical variables of the scenario.Then,the distributions of critical variables are statistically fitted.The genetic algorithm is used to calculate the optimal IS parameters by solving an optimization problem.Considering the error of distribution fitting,the result is modified so that it can accurately reveal the safety benefits of automated vehicles in the real world.Findings–Based on the naturalistic driving data in Shanghai,the proposed method is validated by simulation.The result shows that compared with the existing methods,the proposed method improves the test efficiency by 35 per cent,and the accuracy of accelerated test result is increased by 23 per cent.Originality/value–This paper has three contributions.First,the genetic algorithm is used to calculate IS parameters,which improves the efficiency of test.Second,the result of test is modified by the error correction parameter,which improves the accuracy of test result.Third,typical high-risk cut-in scenarios in China are analyzed,and the proposed method is validated by simulation.

Behavioral adaptation of drivers when driving among automated vehicles

Purpose–This paper aims to explore whether drivers would adapt their behavior when they drive among automated vehicles(AVs)compared to driving among manually driven vehicles(MVs).Understanding behavioral adaptation of drivers when they encounter AVs is crucial for assessing impacts of AVs in mixed-traffic situations.Here,mixed-traffic situations refer to situations where AVs share the roads with existing nonautomated vehicles such as conventional MVs.Design/methodology/approach–A driving simulator study is designed to explore whether such behavioral adaptations exist.Two different driving scenarios were explored on a three-lane highway:driving on the main highway and merging from an on-ramp.For this study,18 research participants were recruited.Findings–Behavioral adaptation can be observed in terms of car-following speed,car-following time gap,number of lane change and overall driving speed.The adaptations are dependent on the driving scenario and whether the surrounding traffic was AVs or MVs.Although significant differences in behavior were found in more than 90%of the research participants,they adapted their behavior differently,and thus,magnitude of the behavioral adaptation remains unclear.Originality/value–The observed behavioral adaptations in this paper were dependent on the driving scenario rather than the time gap between surrounding vehicles.This finding differs from previous studies,which have shown that drivers tend to adapt their behaviors with respect to the surrounding vehicles.Furthermore,the surrounding vehicles in this study are more“free flow’”compared to previous studies with a fixed formation such as platoons.Nevertheless,long-term observations are required to further support this claim.