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The demand for autonomous electric public transport is increasing globally. New vehicle models and variants are increasing the development complexity and hence the overall development time. Therefore, there is a requirement for a vehicle-packaging tool that can translate user inputs into an optimised vehicle package and can be visualised instantly. The aim of this paper is to develop a parametric tool for designing different concepts for electric autonomous buses. The scope of application is intended in the early phase of the vehicle development process to enable a fast and efficient creation of a flexible bus concept. Through a graphical user interface (GUI), the user is able to size and select all the required components for the autonomous vehicle concepts. The vehicle specification is initiated by selection of one of the three classes of vehicle, the desired number of passengers and then the bus interior’s seat arrangement design. An appropriate powertrain and chassis will then automatically be configured in the next steps. The HVAC simulation allows for the design of different components of the refrigeration circuit to ensure proper cabin temperature. For evaluating the concept, the energy consumption is analysed through simulations, and an estimated initial cost of vehicle concepts and individual systems completes the concept. A spider chart summarises all characteristics and offers an overview of the vehicle concept, providing the possibility to compare with other concepts simultaneously. The tool can create 9600 different bus concepts and provides interfaces for expansion.

With the improvement of automated vehicle technology, it is expected that automated vehicles and Human-driven vehicles will share road traffic for a long time in the future. For some low-speed autonomous driving vehicles (<14km/h), which developed for the elderly, one obvious problem is that if they are driven on public roads for a long time, they will affect the normal traffic flow, reduce the efficiency of traffic, and also cause a decrease in social acceptability. In this paper, we aim to study of avoidance strategy for low-speed automated vehicles to give the way for the approaching rear vehicles, which is expected to avoid the dissatisfaction of the following vehicle as much as possible, and without a large delay in the arrival time of the ego-vehicle. A vehicle deceleration behavior dataset is developed by using the naturalistic trajectory data of human drivers collected on the Urban Streets in the United States, and extract the deceleration behavior of normal drivers when they noticed a slower car ahead and built those data into a new dataset. Based on the regression analysis of the database, we propose a novel dynamic avoidance mobility model for the following car. It can dynamically adjust the timing of avoidance according to the velocity and headway of the following car. The experimental results show that our method can achieve a relatively low delay of the ego-car arrival time while the proportion of the rear driver’s dissatisfaction is kept at a low level

Focusing on the preceding vehicle following scene, this paper describes the individual adaptation support control design of the drive-by-wire system of the hybrid electric vehicle for the purpose of enhancing fuel economy. To study support measures that can improve fuel efficiency and optimize driver acceptance, the relationship between the driving characteristics and fuel efficiency characteristics of each driver was clarified. A preceding vehicle following experiment was conducted by using a driving simulator, and the correlation between the driver’s driving characteristics and fuel consumption was analyzed from this result, and the fuel-efficient driving support effect of the drive-by-wire system was considered from the viewpoint of control theory.

In this paper, path construction and path following methods are proposed. The path construction method addresses the problem to generate a path satisfying boundary conditions which are assigned points and tangent directions at the endpoints of the path. This problem is so-called G1 Hermite interpolation, and it has been widely researched, for example, in the area of computer-aided design. The proposed path construction method utilizes one of them, namely the biarc interpolation, as an initial guess for obtaining an optimal path. Trials in some initial conditions show that the proposed method can generate a smooth path with a low computational cost. Meanwhile, the path following method assumes that a linear bicycle model follows a reference path by using the set-point regulator. The proposed method smooths the curvature profile of the reference path to be suited for the path following. Simulation results show that the proposed path construction and following methods achieve smoother tracking than the case without optimal path and smoothing.

The automotive industry concerns about improving road safety. One of the major challenges is to assess road risk and react accordingly in order to avoid accidents. This requires predicting the evolution of the surrounding vehicle trajectories. However, the prediction involves uncertainties from driver operations and ground situations. It is critical to obtain the vehicle trajectory prediction with probabilistic-guarantee bounds. This contribution paper proposes a novel approach to obtain probabilistic ellipsoidal bounds for vehicle trajectory prediction. The vehicle dynamics model adopts a classical bicycle model. The uncertainty of the future trajectory is from the driver’s intend and road condition which can be simplified by setting some parameters of the vehicle dynamics model as a stochastic model. Then, a stochastic optimization problem is formulated to obtain the probabilistic ellipsoidal bounds on the future vehicle trajectories. The proposed approach is validated in a numerical simulation which shows the relationship between the computation complexity and the conservatism of the probabilistic ellipsoidal bounds. The proposed method can be generally used for a physics-based motion method, maneuver-based motion method, and interaction-aware motion method by defining the probability distribution of uncertain variables differently.

The collision avoidance (CA) system is a pivotal part of the autonomous vehicle. Ability to navigate the vehicle in various hazardous scenarios demands reliable actuator interventions. In a complex CA scenario, the increased nonlinearity requires a dependable control strategy. For example, during collisions with a sudden appearing obstacle (i.e. crossing pedestrian, vehicle), the abrupt increment of vehicle longitudinal and lateral forces summation during the CA maneuver demands a system with the ability to handle coupled nonlinear dynamics. Failure to address the aforementioned issues will result in collisions and near-miss incidents. Thus, to solve these issues, a nonlinear model predictive control (NMPC)-based path tracking strategy is proposed as the automated motion guidance for the host vehicle CA architecture. The system is integrated with the artificial potential field (APF) as the motion planning strategy. In a hazardous scenario, APF measures the collision risks and formulates the desired yaw rate and deceleration metrics for the path replanning. APF ensures an optimal replanned trajectory by including the vehicle dynamics into its optimization formulation. NMPC then acts as the coupled path and speed tracking controller to enable vehicle navigation. To accommodate vehicle comfort during the avoidance, NMPC is constrained. Due to its complexity as a nonlinear controller, NMPC can be time-consuming. Therefore, a move blocking strategy is assimilated within the architecture to decrease the system’s computational burden. The modular nature of the architecture allows each strategy to be tuned and developed independently without affecting each others’ performance. The system’s tracking performance is analyzed by computational simulations with several CA scenarios (crossing pedestrian, parked bus, and sudden appearing moving vehicle at an intersection). NMPC tracking performance is compared to the nominal MPC and linear controllers. The effect of move blocking strategies on NMPC performance are analyzed, and the results are compared in terms of mean squared error values. The inclusion of nonlinear tracking controllers in the architecture is shown to provide reliable CA actions in various hazardous scenarios. The work is important for the development of a reliable controller strategy for multi-scenario CA of the fully autonomous vehicle.

The growth of automation sector has brought numerous autonomous technology developments in many sectors, especially automotive. The autonomous features are proven to be helpful in reducing road fatalities globally. Advanced Driver Assistance Systems (ADAS), a system which helps the driving process automation, has growing roles in recent road vehicle features. It is the base for the development of a fully autonomous vehicle. One of the main features of ADAS is Collision Avoidance (CA) system. A sufficient CA architecture usually encompasses threat assessment, path planning and path tracking strategies. There are many ways of developing precise CA architecture using the combination of these strategies. This paper aims to review current available methods for CA as an introductory idea for researchers who are new to this field. Each of the methods in each strategy is categorised into several groups. Their advantages and drawbacks are discussed. In addition to that, several improvement suggestions for a comprehensive CA system were highlighted.

Ensuring a safe mobility for elderly drivers is one of the important issues to be addressed for supporting the daily life of elderly. Shared control of a risk predictive braking maneuver attempts to manage the potential risk of crashing with respect to hazards that cannot be seen by the driver. The assistance system performs a partial deceleration maneuver to achieve a referenced velocity in uncertain situations, such as one in which an unobserved pedestrian might initiate a road crossing. This paper presents the following: first, an analysis of hazard-anticipatory driving for an expert driver; second, a detailed description of risk predictive control; third, the effectiveness and limitations of risk predictive control obtained through a numerical simulation; fourth, the framework of the driver assistance system; and fifth, experimental data for evaluation of proactive safety performance. Specifically, three questions will be investigated: How would expert drivers drive in uncertain situations?; How can a reference velocity be determined?; and How can a system be designed so that the relationship between the human and the machine is complementary? Based on this framework, this paper discusses a shared control system that dynamically shares control authority between the elderly driver and the ADAS.

Elderly drivers are at particular risk for accidents due to age-related visual, cognitive, and physical impairments. This study proposes a risk predictive shared steering control based on potential risk prediction of collision with vulnerable road users, such as cyclists on urban roads. Under the driving simulator experiment, we investigated its acceptance and the effectiveness of providing the auditory alert and visual information, and implementing the shared steering control in the hidden risk situation.

Recently, automatic steering systems for emergency obstacle avoidance have been studied extensively. The control input of such active steering control systems can be classified into the steering angle and the steering torque input. The steering torque based control provides some degree of freedom for drivers to control the vehicle motion, thus it has potential for development as steering assistance system. This paper describes the evaluation of shared control characteristics between human drivers and the active steering system for obstacle avoidance assistance system based on steering torque input. The shared control characteristics between the driver and the active steering system are investigated by using the driving simulator reconstructing a dangerous scenario.

This paper presents a driver assistance system of lane change manoeuvre on highway. The assistance system is designed based on a reference driver model which employs common on-board vehicle sensing signals and determines the initiation timing and the magnitude of longitudinal and lateral accelerations. The assistance system is designed to be able to accelerate automatically in longitudinal direction and provide a beep sound to cue the initiation timing of steering manoeuvre by considering the human reaction time. The feasibility of the proposed assistance system is tested on a Driving Simulator (DS). The experimental scenarios are indicated in detail and the obtained results are statistically analysed from the individual and overall aspects of the conducted experiments.

It has been reported that the most mental state of the drivers during traffic accidents in Japan is that the driver being hurry. In this paper, we present the analysis and monitoring system especially developed for hurry driving in several driving situations. The most effective driving feature in each situation has been selected based on the comparative statistical analysis of the driver behavior between the normal and hurry driving conditions. It is found that the features that classify the hurry driving well in most situations are the time headway, the host vehicle velocity and the longitudinal acceleration. Through these features, the appropriate classification rules are determined, which allows us to later establish the hurry-driving detection algorithm, which is in turn used to develop the hurry driving monitoring system based upon the driving-state recognition module. The algorithm is validated by offline simulations with collected driving data, and then evaluated online using an experiment vehicle. The results clearly confirm the practicability and effectiveness of the proposed algorithm.