Human–Machine Cooperative Control of Intelligent Vehicles for Lane Keeping—Considering Safety of the Intended Functionality

Reasonably foreseeable misuse by persons, as a primary aspect of safety of the intended functionality (SOTIF), has a significant effect on cooperation performance for lane keeping. This paper presents a novel human–machine cooperative control scheme with consideration of SOTIF issues caused by driver error. It is challenging to balance lane keeping performance and driving freedom when driver error occurs. A safety evaluation strategy is proposed for safety supervision, containing assessments of driver error and lane departure risk caused by driver error. A dynamic evaluation model of driver error is designed based on a typical driver model in the loop to deal with the uncertainty and variability of driver behavior. Additionally, an extension model is established for determining the cooperation domain. Then, an authority allocation strategy is proposed to generate a dynamic shared authority and achieve an adequate balance between lane keeping performance and driving freedom. Finally, a model predictive control (MPC)-based controller is designed for calculating optimal steering angle, and a steer-by-wheel (SBW) system is employed as an actuator. Numerical simulation tests are conducted on driver error scenarios based on the CarSim and MATLAB/Simulink software platforms. The simulation results demonstrate the effectiveness of the proposed method.

Relationship between Programmed Heavy Vehicle Inspections and Traffic Safety

Heavy vehicle crashes incur significant economic and social costs. Although most crashes are considered to be related to driver error, the effects of vehicle defects are major in many crashes. Therefore, various vehicle inspections including Queensland’s Certificate of Inspection (COI) scheme have been implemented to improve the safety of heavy vehicles. This study analyzes the trends of heavy vehicle crashes and their relationships with COI results. Longitudinal data provided by Queensland’s Department of Transport and Main Roads for the period of June 2009 through December 2013 were used to perform the analyses. The data include 474,640 programmed inspections and 2,274 crashes in which heavy vehicles were involved. The results show significant relationships between the monthly average inspection failure rate as well as the monthly average failure severity level, and the total number of heavy vehicle crashes. The results also reveal significant relationships between the monthly average inspection failure rate, average vehicle age, as well as monthly average mean maximum temperature, and the number of defect-related crashes. The implications of these results are discussed with respect to heavy vehicle safety policies.

Updating our understanding of situation awareness in relation to remote operators of autonomous vehicles

The introduction of autonomous vehicles (AVs) could prevent many accidents attributable to human driver error. However, even entirely driverless vehicles will sometimes require remote human intervention. Current taxonomies of automated driving do not acknowledge the possibility of remote control of AVs or the challenges that are unique to such a driver in charge of a vehicle that they are not physically occupying. Yet there are significant differences between situation awareness (SA) in normal driving contexts and SA in these remote driving operations. We argue that the established understanding of automated driving requires updating to include the context of remote operation that is likely to come in to play at higher levels of automation. It is imperative to integrate the role of the remote operator within industry standard taxonomies, so that regulatory frameworks can be established with regards to the training required for remote operation, the necessary equipment and technology, and a comprehensive inventory of the use cases under which we could expect remote operation to be carried out. We emphasise the importance of designing control interfaces in a way that will maximise remote operator (RO) SA and we identify some principles for designing systems aimed at increasing an RO’s sense of embodiment in the AV that requires temporary control.

Motorist Understanding of Pavement Centre Lines and their Effect on Driving Behaviour

Human factors and more generally driver errors account for the largest number of road accidents. Driver errors are external human factors that can contribute to specific error types selected from slip, lapse, mistake and violation. Action and information retrieval errors are both examples of driver errors. The failure to interpret correctly an intended road marking’s message causes driver misunderstanding and lead to a driver error. Centre lines are examples of such markings and if misread or unrecognised may cause unintentional driver violations and unsafe driving. This study focused on the examining of driver understanding of road markings, and the influence of centre lines on their driving behaviour. This study determined that drivers had a much better understanding of the overtaking messages intended by road markings, than the directional flow message. Drivers demonstrated that they relied more on signs and other drivers to determine whether the road is a two-way or not. This study demonstrated that the presence of both centre lines and edge lines have a positive effect on a driver in handling and controlling of their vehicles’ position. It was postulated from this study that the absence of the edge lines has a more significant effect on a vehicle’s position than the absence of centre lines.

Futuramas of the present: the “driver problem” in the autonomous vehicle sociotechnical imaginary

The visions surrounding “self-driving” or “autonomous” vehicles are an exemplary instance of a sociotechnical imaginary: visions of a future technology that has yet to be developed or is in the process of development. One of the central justifications for the development of autonomous vehicles is the claim that they will reduce automobility related death and injury. Central to this narrative is the assumption that more than 90% of road crashes are the result of “driver error.” This paper describes the process by which this statistic has been constructed within road safety research and subsequently accepted as a received fact. It is one of the principal semiotic components of the autonomous vehicle sociotechnical imaginary: if human drivers are responsible for ~90% of road crashes, autonomous vehicles should in principle be able to reduce road death and injury rates by a similar percentage. In this paper, it is argued that death and injury are not an aggregate of events that can be distributed across the three central variables of traditional road safety research: the driver, the vehicle, and the environment. The autonomous vehicle sociotechnical imaginary has embraced the central assumption of road safety research, that road violence is not an intrinsic property of automobility but is contingent because largely due to driver error. On the basis of this assumption it has been possible to configure autonomous vehicles as the solution to road violence. Although sociotechnical imaginaries are typically oriented towards the future, it is the significance of the autonomous vehicle sociotechnical imaginary in the present that is the focus of this paper. Autonomous vehicles are not the radically transformational technology their proponents claim but simply the most recent of a succession of automobility sociotechnical imaginaries. They are not transformational because their promotion ensures the continued reproduction of more of the same: namely, more automobility.

Analysis Young Driver Behavior in “Z” Generation

The magnitude of the cause of the accident due to driver error causes the need to do an analysis related to the characteristics of the driver and the factors that most influence the cause of the accident and then the road. At this time the young driver is in generation Z who has an age between 16-21 years. Then an analysis of the characteristics of the causes of traffic accidents, especially in the "Z" generation of drivers by using the driver simulator "Teknosim" and the results of the analysis of observations through crosstab models and chi square test to the influential variable. Obtained the characteristics of the generation driver "Z" in low traffic tend to move lane under the right conditions, brake suddenly with very minimal dexterity, this causes collisions with other vehicles with a very short reaction time without agility of 0.012 minutes, and not yet can improve how to drive and can not prevent wasteful consumption of fuel even in traffic that is not dense with Asymp.Sig values. (2-sided) in the amount of 0.010 - 0.014. In heavy traffic conditions, the "Z" Generation driver has the characteristic of tapping the horn and tends to brake suddenly with a very small braking distance or too close to the vehicle in front of him and very minimal dexterity with the Asymp.Sig value. (2-sided) in the amount of 0.014 - 0.017.

Crash Data-Based Investigation into How Injury Severity Is Affected by Driver Errors

Unsafe driving behaviors, driver limitations, and conditions that lead to a crash are usually referred to as driver errors. Even though driver errors are widely cited as a critical reason for crash occurrence in crash reports and safety literature, the discussion on their consequences is limited. This study aims to quantify the effect of driver errors on crash injury severity. To assist this investigation, driver errors were categorized as sequential events in a driving task. Possible combinations of driver error categories were created and ranked based on statistical dependences between error combinations and injury severity levels. Binary logit models were then developed to show that typical variables used to model injury severity such as driver characteristics, roadway characteristics, environmental factors, and crash characteristics are inadequate to explain driver errors, especially the complicated ones. Next, ordinal probit models were applied to quantify the effect of driver errors on injury severity for rural crashes. Superior model performance is observed when driver error combinations were modeled along with typical crash variables to predict the injury outcome. Modeling results also illustrate that more severe crashes tend to occur when the driver makes multiple mistakes. Therefore, incorporating driver errors in crash injury severity prediction not only improves prediction accuracy but also enhances our understanding of what error(s) may lead to more severe injuries so that safety interventions can be recommended accordingly.

The Automated and Electric Vehicles Act 2018 Part 1 and Beyond: A Critical Review

On 19 July 2018, the Automated and Electric Vehicles Act 2018 (AEVA) received Royal Assent. As motor vehicles are becoming increasingly technologically based, with driving aids having taken over many of the more mundane (and possibly dangerous) aspects of driving from the driver, it is imperative that legislation keeps pace to determine the responsibilities of the parties. Motor insurance provides protection for those involved with vehicles and who may suffer harm, injury, and loss due to the negligence of the actors. This is most frequently driver error, but may also include manufacturing defects, which result in deaths and less serious injuries. It is also here where the intersection between torts and insurance laws needs careful management. It would be particularly unfair to ask drivers or third-party victims of motor vehicle accidents to seek redress from a manufacturer for losses incurred during the actions of an autonomous vehicle. Consumer law has historically removed this burden from affected consumers and it is entirely sensible for the law to protect individuals in an emerging field—and perhaps even more so given the trajectory of vehicles with driver-enabled qualities and the numbers of vehicles currently featuring such innovations. Yet, the AEVA consists of aspects which are troubling in respect of the motor insurance industry’s dominance of this market, the application of compulsory insurance, and exclusions and limitations to responsibility which expose policy holders and victims to EU-breaching levels of risk.

Benefits and Costs of Ride-Sharing in Shared Automated Vehicles across Austin, Texas: Opportunities for Congestion Pricing

A self-driving, fully automated, or “autonomous” vehicle (AV) revolution is imminent, with the potential to eliminate driver costs and driver error, while ushering in an era of shared mobility. Dynamic ride-sharing (DRS), which refers to sharing rides with strangers en route, is growing, with top transportation network companies providing such services. This work uses an agent-based simulation tool called MATSim to simulate travel patterns in Austin, Texas in the presence of personal AVs, and shared AVs (SAVs), with DRS and advanced road-pricing policies in place. Fleet size, pricing, and fare level impacts are analyzed in depth to provide insight into how SAVs may best be introduced to a city or region. Results indicate that the cost-effectiveness of traveling with strangers overcomes inconvenience and privacy issues at moderate-to-low fare levels, with high fares being more detrimental than the base case. A moderately sized Austin, Texas fleet (one SAV for every 25 people) serves nearly 30% of all trips made during the day. The average vehicle occupancy of this fleet was around 1.48 [after including the 12.7% of SAV vehicle-miles traveled (VMT) empty/without passengers], with a 4.5% increase in VMT. This same fleet performs better when road-pricing is enforced in the peak periods (4 h a day), moderating VMT by 2%, increasing SAV demand and in turn fleet-manager revenue. SAVs are able to earn around $100 per SAV per day even after paying tolls, but only at low-fare levels.

Braking System Automation on Cars using a Distance Sensor

The factor of driver error in driving (human error) is one of the causes of the high number of traffic accidents in the present. To anticipate this, the discipline and concentration of the driver when driving is needed. However, when drivers have discipline and sufficient concentration of accidents can still occur. Therefore, the quality of the vehicle security system also greatly affects safety when driving. With the development of science and technology today it is very possible for humans to make security systems in vehicles. Accidents are often caused by the driver being unable to react quickly when there is a sudden blocking of objects. Especially when the driver is driving at high speed. This can be overcome by automating the braking system, so that the vehicle's speed will slow down even though the driver does not step on the brake lever. So that the possibility of an accident can be avoided. And automation is expected to be implemented in vehicles widely. In this study, designed a brake automation system using fuzzy logic by making a prototype. This prototype serves as a visual aid to evaluate the workings of the membership functions that will be used. From this evaluation it is known the value of each membership function. These values ​​are used as the main parameters in determining the Rules that will affect the output value of the brake force. With these Rules, automation of the brake system can work optimally. And it is known that the distance of the prototype car can run between other obstacle with a width of 50cm and the braking of the prototype car stops completely when the prototype is ± 5-10 cm with the obstacle in front of it. Keywords: Automation of Brake Systems, Fuzzy Logic, Distance, Speed.