Automated Road Vehicles Research Articles

Models of Human Behavior for Human-Robot Interaction and Automated Driving: How Accurate Do the Models of Human Behavior Need to Be?

There are many examples of cases where access to improved models of human behavior and cognition has allowed creation of robots which can better interact with humans, and not least in road vehicle automation this is a rapidly growing area of research. Human-robot interaction (HRI) therefore provides an important applied setting for human behavior modeling - but given the vast complexity of human behavior, how complete and accurate do these models need to be? Here, we outline some possible ways of thinking about this problem, starting from the suggestion that modelers need to keep the right end goal in sight: A successful human-robot interaction, in terms of safety, performance, and human satisfaction. Efforts toward model completeness and accuracy should be focused on those aspects of human behavior to which interaction success is most sensitive. We emphasise that identifying which those aspects are is a difficult scientific objective in its own right, distinct for each given HRI context. We propose and exemplify an approach to formulating a priori hypotheses on this matter, in cases where robots are to be involved in interactions which currently take place between humans, such as in automated driving. Our perspective also highlights some possible risks of overreliance on machine-learned models of human behavior in HRI, and how to mitigate against those risks.

Hierarchical motion control strategies for handling interactions of automated vehicles

The paper proposes motion control strategies for automated road vehicles to handle interactions among vehicles. The control strategies are built in a hierarchical structure, which contains a high-level learning-based control, a low-level robust control and an optimization-based supervisor. The purposes of the control strategies are to guarantee collision-free motion of the vehicles, and moreover to improve economy and traveling time performance levels. In the paper two configurations of the hierarchical structure are proposed, which are based on the centralized and independent design for automated vehicle motion. The paper presents the comparison of the two configurations through simulation examples. Moreover, the operation of the independent configuration on a small-scaled indoor test vehicle platform is also presented.

Decision Fault Tree Learning and Differential Lyapunov Optimal Control for Path Tracking

This paper considers the main challenges for all components engaged in the driving task suggested by the automation of road vehicles or autonomous cars. Numerous autonomous vehicle developers often invest an important amount of time and effort in fine-tuning and measuring the route tracking to obtain reliable tracking performance over a wide range of autonomous vehicle speed and road curvature diversities. However, a number of automated vehicles were not considered for fault-tolerant trajectory tracking methods. Motivated by this, the current research study of the Differential Lyapunov Stochastic and Decision Defect Tree Learning (DLS-DFTL) method is proposed to handle fault detection and course tracking for autonomous vehicle problems. Initially, Differential Lyapunov Stochastic Optimal Control (SOC) with customizable Z-matrices is to precisely design the path tracking for a particular target vehicle while successfully managing the noise and fault issues that arise from the localization and path planning. With the autonomous vehicle's low ceilings, a recommendation trajectory generation model is created to support such a safety justification. Then, to detect an unexpected deviation caused by a fault, a fault detection technique known as Decision Fault Tree Learning (DFTL) is built. The DLS-DFTL method can be used to find and locate problems in expansive, intricate communication networks. We conducted various tests and showed the applicability of DFTL. By offering some analysis of the experimental outcomes, the suggested method produces significant accuracy. In addition to a thorough study that compares the results to state-of-the-art techniques, simulation was also used to quantify the rate and time of defect detection. The experimental result shows that the proposed DLS-DFTL enhances the fault detection rate (38%), reduces the loss rate (14%), and has a faster fault detection time (24%) than the state of art methods.

Analysis and Synthesis of Coordinated Control Systems for Automated Road Vehicles

Through the automation of road vehicles, several smart actuators have been developed, with which various automated and autonomous functionalities can be performed [...]

Shared Automated Electric Vehicle Prospects for Low Carbon Road Transportation in British Columbia, Canada

This study explores the long-term energy use implications of electrification, automation and sharing of road vehicles in British Columbia, Canada. Energy use is first analyzed for the years 1990–2016 for forward forecasting, and hypothetical scenarios ranging from conservative to disruptive, incorporating various effects of road vehicle electrification, sharing and automation, as well as influences of other technology disruptions, such as online shopping and e-learning are presented and used to project the road transportation energy use in B.C. to 2060. Transportation energy use projections are compared to those of the Canadian Energy Regulator (CER). When considering only the effect of vehicle electrification, the scenarios show higher energy savings compared to CER’s scenarios. The combined impact of vehicle electrification and automation leads to decreased energy use to 2060 for all scenarios considered. The energy savings for all scenarios, except for the conservative one, are higher than CER’s projections. When the effects of vehicle electrification, automation and sharing are merged, all scenarios yield energy savings beyond the CER projections. Inclusion of other technology disruptions and the effects of pandemics like COVID-19 reduce transportation demand and provide further energy savings. The BAU scenario given in this study shows energy use decreases compared to 2016 of 26.3%, 49%, 62.24%, 72.1% for the years 2030, 2040, 2050, and 2060 respectively.

Optimization-based path-planning for connected and non-connected automated vehicles

A path-planning algorithm for connected and non-connected automated road vehicles on multilane motorways is derived from the opportune formulation of an optimal control problem. In this framework, the objective function to be minimized contains appropriate respective terms to reflect: the goals of vehicle advancement; passenger comfort; and avoidance of collisions with other vehicles and of road departures. Connectivity implies, within the present work, that connected vehicles can exchange with each other (V2V) real-time information about their last generated short-term path. For the numerical solution of the optimal control problem, an efficient feasible direction algorithm (FDA) is used. To ensure high-quality local minima, a simplified Dynamic Programming (DP) algorithm is also conceived to deliver the initial guess trajectory for the start of the FDA iterations. Thanks to very low computation times, the approach is readily executable within a model predictive control (MPC) framework. The proposed MPC-based approach is embedded within the Aimsun microsimulation platform, which enables the evaluation of a plethora of realistic vehicle driving and advancement scenarios under different vehicles mixes. Results obtained on a multilane motorway stretch indicate higher efficiency of the optimally controlled vehicles in driving closer to their desired speed, compared to ordinary manually driven vehicles. Increased penetration rates of automated vehicles are found to increase the efficiency of the overall traffic flow, benefiting manual vehicles as well. Moreover, connected controlled vehicles appear to be more efficient in achieving their desired speed, compared also to the corresponding non-connected controlled vehicles, due to the improved real-time information and short-term prediction achieved via V2V communication.

AI Perspectives in Smart Cities and Communities to Enable Road Vehicle Automation and Smart Traffic Control

Smart cities and communities (SCC) constitute a new paradigm in urban development. SCC ideate a data-centered society aimed at improving efficiency by automating and optimizing activities and utilities. Information and communication technology along with Internet of Things enables data collection and with the help of artificial intelligence (AI) situation awareness can be obtained to feed the SCC actors with enriched knowledge. This paper describes AI perspectives in SCC and gives an overview of AI-based technologies used in traffic to enable road vehicle automation and smart traffic control. Perception, smart traffic control and driver modeling are described along with open research challenges and standardization to help introduce advanced driver assistance systems and automated vehicle functionality in traffic. To fully realize the potential of SCC, to create a holistic view on a city level, availability of data from different stakeholders is necessary. Further, though AI technologies provide accurate predictions and classifications, there is an ambiguity regarding the correctness of their outputs. This can make it difficult for the human operator to trust the system. Today there are no methods that can be used to match function requirements with the level of detail in data annotation in order to train an accurate model. Another challenge related to trust is explainability: models can have difficulty explaining how they came to certain conclusions, so it is difficult for humans to trust them.

Estimation of Driver Lane Change Intention Based on the LSTM and Dempster–Shafer Evidence Theory

Rapid and correct estimation of driver lane change intention plays an important role in the advanced driver assistance system (ADAS), which could make the driver improve the reliability of the ADAS system and help to decrease driver workload. In this study, a method based on the long short-term memory network (LSTM) and Dempster–Shafer evidence theory is proposed. The model consists of a preliminary decision-making label and a final decision-making label. Driver visual information, head orientation, and vehicle dynamics are collected by preliminary decision-making label. Then, LSTM is used to calculate the initial probability of the driver lane change (left, right, and lane keeping) maneuver intention. The outputs of LSTM are normalized and assigned a basic probability by the Dempster–Shafer evidence theory. The final decision-making label analyzes the information and outputs the probability of each lane change intention and the decision is to identify the driver's current intention. The experimental results show that the accuracy of the model is 90.7% for the intention of changing left and 89.1% for the intention of changing right. The outcome of this work is an essential component for all levels of road vehicle automation.

Reduction of Pedestrian Accidents – Automated Road Vehicles

The number of traffic accidents is decreasing thanks to measures taken by the government and also by car manufacturers. However, despite the measures, thousands of people die on the roads every year. The most vulnerable group of road users are pedestrians. In this article we will deal with the measures and ways of reducing the occurrence of traffic accidents by pedestrian-vehicle, or at least reducing the consequences of a traffic accident. It is important to point out the development of traffic accidents not only in Slovakia but also in the EU and to point out the possibilities of elimination or improvement in this area. One of the innovative ways to reduce the number of accidents, in general, is automated road vehicles. Technologies and procedures used in automated road vehicles can significantly reduce accidents. For this reason, the basic technological systems are being developed, which contribute to the reduction of the accidents. The hardware and software equipment of automated road vehicles will be described in this article. It is also necessary to focus on the detection of pedestrians by an automated road vehicle, where machine learning and algorithms are used to calculate an estimate the direction of pedestrians and their movement in the traffic.

Ranking the Key Areas for Autonomous Proving Ground Development Using Pareto Analytic Hierarchy Process

Autonomous or highly automated road vehicles and all related technologies are under intensive research and development. Moreover, internationally a massive investment increase can be observed in the automotive industry. According to this megatrend, new automotive test tracks appear or older ones transform to be capable of testing and proving for autonomous vehicles. Therefore, the question emerges: what are the key areas for automated drive development, which must be financed in case of autonomous proving ground design? It is a real challenge to be able to make the right decisions due to a lack of numerous experiences in this field. In this research, experts of automated driving technology have been surveyed and their opinion and knowledge have been synthesized. As a strong purpose of gaining robust results, the conventional AHP has been amended by the Pareto approach to ensure that the derived weights correspond to the expert scoring intention so perfectly that it cannot be more improved. Since the non-Pareto optimal weight results might cause rank reversal in the final prioritization, the applied Pareto test guarantees that the final outcome reflects the expert evaluators’ incentive. The conducted analysis has indicated that the obtained results are robust not only from the sensitivity point of view but also from the Pareto optimality approach. The proposed hierarchical decision model is therefore applicable to assist decision making for autonomous proving ground developments. The main contribution of the article, however, is to present the first reliable prioritization of the autonomous proving ground elements to extend the body of professional knowledge.

Opportunities and Challenges in Cooperative Road Vehicle Automation

This paper provides an introduction to the opportunities for improving the performance of road transportation automation systems by use of vehicle-vehicle and vehicle-infrastructure communication and cooperation. Four different types of cooperative driving automation are defined and examples of the functionality enabled by each are described. Although the benefits of cooperative automation are significant, there are also significant challenges to its widespread deployment, which are also described. The risks of over-reliance on communication for cooperative automation implementations are also discussed to provide a balanced view of appropriate levels of cooperation.

Communication and Interaction With Semiautonomous Ground Vehicles by Force Control Steering.

While full automation of road vehicles remains a future goal, shared-control and semiautonomous driving-involving transitions of control between the human and the machine-are more feasible objectives in the near term. These alternative driving modes will benefit from new research toward novel steering control devices, more suitably where machine intelligence only partially controls the vehicle. In this article, it is proposed that when the human shares the control of a vehicle with an autonomous or semiautonomous system, a force control, or nondisplacement steering wheel (i.e., a steering wheel which does not rotate but detects the applied torque by the human driver) can be advantageous under certain schemes: tight rein or loose rein modes according to the H -metaphor. We support this proposition with the first experiments to the best of our knowledge, in which human participants drove in a simulated road scene with a force control steering wheel (FCSW). The experiments exhibited that humans can adapt promptly to force control steering and are able to control the vehicle smoothly. Different transfer functions are tested, which translate the applied torque at the FCSW to the steering angle at the wheels of the vehicle; it is shown that fractional order transfer functions increment steering stability and control accuracy when using a force control device. The transition of control experiments is also performed with both: a conventional and an FCSW. This prototypical steering system can be realized via steer-by-wire controls, which are already incorporated in commercially available vehicles.

Set-Based Prediction of Traffic Participants Considering Occlusions and Traffic Rules

Provably safe motion planning for automated road vehicles must ensure that planned motions do not result in a collision with other traffic participants. This is a major challenge in autonomous driving, since the future behavior of other traffic participants is not known and since traffic participants are often hidden due to occlusions. In this work, we propose a formal set-based prediction that contains all acceptable future behaviors of both detected and potentially hidden traffic participants. Based on formalized traffic rules and nondeterministic motion models, we perform reachability analysis to predict the set of possible occupancies and velocities of vehicles, pedestrians, and cyclists. Real-world experiments with a test vehicle in various traffic situations demonstrate the applicability and real-time capability of our over-approximative prediction for both online verification and fail-safe trajectory planning. Even in congested, complex traffic scenarios, our forecasting approach enables self-driving vehicles to never cause accidents.

Theme issue on connected and automated road vehicles

In recent years, Connected and Automated Vehicles (CAVs) have become a major focus of research and development efforts in the automotive industry. CAVs are expected to significantly impact the tran...

Connected and automated road vehicles: state of the art and future challenges

ABSTRACTThe state of the art of modelling, control, and optimisation is discussed for automated road vehicles that may utilise wireless vehicle-to-everything (V2X) connectivity. The appropriate tools to address safety and energy efficiency are described and the effects on traffic dynamics are highlighted. Finally, the economical and societal impacts of the deployment of connected and automated vehicles are discussed.

Reprint of: The importance of user perspective in the evolution of MaaS

• MaaS is a promising but hyped phenomenon in its nascent state. • MaaS by name is new but is an evolution in the ongoing pursuit of integrated transport. • A taxonomy of MaaS integration underlines different levels of service provision to the user. • Users have a hierarchy of need from MaaS in which viable travel options remain key. • Behaviour insights can inform the prospects for consideration and adoption of MaaS. The rapid emergence of Mobility as a Service (MaaS) into the transport sector’s lexicon has brought with it an air of expectation that suggests a future mobility revolution . This paper focusses on the user perspective and offers a deepening of socio-technical thinking about MaaS and its prospects. It first provides an examination of what is understood to date about MaaS in what is a new but rapidly evolving body of literature. This highlights the concept of MaaS as a ‘mobility system beyond the private car’ and the new centrality of a ‘mobility intermediary’ layer in that system. The paper then focuses and elaborates upon its contention that MaaS is neither new nor revolutionary but is rather an evolutionary continuation in terms of transport integration. Emerging from an era of unimodal travel information systems becoming multimodal and then integrated multimodal information services, MaaS is now about adding seamless booking, payment and ticketing to the integration offer. The paper puts forward a ‘Levels of MaaS Integration (LMI) taxonomy’ analogous to the level 0–5 SAE taxonomy for automation of road vehicles. This taxonomy, designed around the user perspective (including cognitive user effort), concerns operational, information and transactional integration that it is suggested reflect a hierarchy of user need. From a synthesis of insights from the ‘pre-MaaS’ literature concerning choice making for travel and the role of information, a MaaS behavioural schema is provided to illustrate potential consideration and adoption of MaaS from the user perspective. In concluding, the paper considers what a user perspective reveals for the future prospects of MaaS and in particular for the mobility intermediaries.

Real-time optimal motion planning for automated road vehicles

Abstract This paper presents a real-time optimal motion planner algorithm for road vehicles. The method is based on a cubic spline trajectory planner which is able to plan a set of vehicle motions driving from a given initial state to a required final state. Maximal dynamical feasibility and passenger comfort are ensured by minimizing the lateral acceleration and tracking errors as the vehicle moves along the trajectory. Tracking of the planned motion is realized during planning and execution as well by separate longitudinal and lateral controllers. efficient implementation and small number of optimization variables enables real-time usage. The trajectory planner is first tested in a quasi real-time simulation environment and then under real working conditions at the dynamic platform of proving ground ZalaZone with a completely drive-by-wire Smart Fortwo. Measurement results are presented and analyzed in detail, and possible future research directions are mentioned.

Action intention recognition of cars and bicycles in intersections

Action intention recognition is becoming increasingly important in the road vehicle automation domain. Autonomous vehicles must be aware of their surroundings if we are to build safe and efficient transport systems. This paper presents a method for predicting the action intentions of road users based on sensors in the road infrastructure. The scenarios used for demonstration are recorded on two different public road sections. The first scenario includes bicyclists and the second includes cars that are driving in a road approaching an intersection where they are either leaving or continuing straight. A 3D camera-based data acquisition system is used to collect trajectories of the road users that are used as input for models trained to predict the action intention of the road users. The proposed system enables future connected and automated vehicles to receive collision warnings from an infrastructure-based sensor system well in advance to enable better planning.

Motion Planning for Highly Automated Road Vehicles with a Hybrid Approach Using Nonlinear Optimization and Artificial Neural Networks

Motion Planning for Highly Automated Road Vehicles with a Hybrid Approach Using Nonlinear Optimization and Artificial Neural Networks

The importance of user perspective in the evolution of MaaS

The rapid emergence of Mobility as a Service (MaaS) into the transport sector’s lexicon has brought with it an air of expectation that suggests a future mobility revolution. This paper focusses on the user perspective and offers a deepening of socio-technical thinking about MaaS and its prospects. It first provides an examination of what is understood to date about MaaS in what is a new but rapidly evolving body of literature. This highlights the concept of MaaS as a ‘mobility system beyond the private car’ and the new centrality of a ‘mobility intermediary’ layer in that system. The paper then focuses and elaborates upon its contention that MaaS is neither new nor revolutionary but is rather an evolutionary continuation in terms of transport integration. Emerging from an era of unimodal travel information systems becoming multimodal and then integrated multimodal information services, MaaS is now about adding seamless booking, payment and ticketing to the integration offer. The paper puts forward a ‘Levels of MaaS Integration (LMI) taxonomy’ analogous to the level 0–5 SAE taxonomy for automation of road vehicles. This taxonomy, designed around the user perspective (including cognitive user effort), concerns operational, informational and transactional integration that it is suggested reflect a hierarchy of user need. From a synthesis of insights from the ‘pre-MaaS’ literature concerning choice making for travel and the role of information, a MaaS behavioural schema is provided to illustrate potential consideration and adoption of MaaS from the user perspective. In concluding, the paper considers what a user perspective reveals for the future prospects of MaaS and in particular for the mobility intermediaries.