A Review of Shared Control for Automated Vehicles: Theory and Applications

The last decade has shown an increasing interest on advanced driver assistance systems (ADAS) based on shared control, where automation is continuously supporting the driver at the control level with an adaptive authority. A first look at the literature offers two main research directions: 1) an ongoing effort to advance the theoretical comprehension of shared control, and 2) a diversity of automotive system applications with an increasing number of works in recent years. Yet, a global synthesis on these efforts is not available. To this end, this article covers the complete field of shared control in automated vehicles with an emphasis on these aspects: 1) concept, 2) categories, 3) algorithms, and 4) status of technology. Articles from the literature are classified in theory- and application-oriented contributions. From these, a clear distinction is found between coupled and uncoupled shared control. Also, model-based and model-free algorithms from these two categories are evaluated separately with a focus on systems using the steering wheel as the control interface. Model-based controllers tested by at least one real driver are tabulated to evaluate the performance of such systems. Results show that the inclusion of a driver model helps to reduce the conflicts at the steering. Also, variables such as driver state, driver effort, and safety indicators have a high impact on the calculation of the authority. Concerning the evaluation, driver-in-the-loop simulators are the most common platforms, with few works performed in real vehicles. Implementation in experimental vehicles is expected in the upcoming years.This work was supported in part by the ECSEL Joint Undertaking, which funded the PRYSTINE project under Grant 783190, and in part by the AUTOLIB project (ELKARTEK 2019 ref. KK-2019/00035; Gobierno Vasco Dpto. Desarrollo económico e infraestructuras)

From the Concept of Being “the Boss” to the Idea of Being “a Team”: The Adaptive Co-Pilot as the Enabler for a New Cooperative Framework

The “classical” SAE LoA for automated driving can present several drawbacks, and the SAE-L2 and SAE-L3, in particular, can lead to the so-called “irony of automation”, where the driver is substituted by the artificial system, but is still regarded as a “supervisor” or as a “fallback mechanism”. To overcome this problem, while taking advantage of the latest technology, we regard both human and machine as members of a unique team that share the driving task. Depending on the available resources (in terms of driver’s status, system state, and environment conditions) and considering that they are very dynamic, an adaptive assignment of authority for each member of the team is needed. This is achieved by designing a technology enabler, constituted by the intelligent and adaptive co-pilot. It comprises (1) a lateral shared controller based on NMPC, which applies the authority, (2) an arbitration module based on FIS, which calculates the authority, and (3) a visual HMI, as an enabler of trust in automation decisions and actions. The benefits of such a system are shown in this paper through a comparison of the shared control driving mode, with manual driving (as a baseline) and lane-keeping and lane-centering (as two commercial ADAS). Tests are performed in a use case where support for a distracted driver is given. Quantitative and qualitative results confirm the hypothesis that shared control offers the best balance between performance, safety, and comfort during the driving task.This research was supported by the ECSEL Joint-Undertaking,which funded the PRYSTINE project under the Grant 783190

Understanding the effects of peripheral vision and muscle memory on in-vehicle touchscreen interactions

It is important to gain a better understanding of how drivers interact with in-vehicle touchscreens to help design interfaces to minimise “eyes off road” time. The study aimed to investigate the relative effects of two interaction mechanisms (peripheral vision - PV and muscle memory - MM) shown to be relevant to visual behaviour when driving, on the time to press different sized buttons (small 6x6cm, medium 10x10cm, large 14x14cm) on an in-vehicle touchscreen. Twenty-five participants took part in a driving simulator study. They were presented with a single, white, square button on the touchscreen on 24 successive trials. For MM conditions, participants wore a pair of glasses that blocked their peripheral vision and for PV conditions they were asked to keep their focus on the vehicle in front throughout. Results showed that task time gradually decreased for the trials when participants could only use MM. However, overall task time for MM conditions were significantly higher than for those in which PV was utilised, and participants rated the use of MM to be more difficult than PV. In contrast, results suggest that for interfaces that utilise peripheral visual processing the learning effect is not evident and operation times are constant over time. These findings indicate that in-vehicle touch screens should be designed to utilise peripheral vision for making simple button selections with reduced visual demand

Understanding the effects of peripheral vision and muscle memory on in-vehicle touchscreen interactions

It is important to gain a better understanding of how drivers interact with in-vehicle touchscreens to help design interfaces to minimise “eyes off road” time. The study aimed to investigate the relative effects of two interaction mechanisms (peripheral vision - PV and muscle memory - MM) shown to be relevant to visual behaviour when driving, on the time to press different sized buttons (small 6x6cm, medium 10x10cm, large 14x14cm) on an in-vehicle touchscreen. Twenty-five participants took part in a driving simulator study. They were presented with a single, white, square button on the touchscreen on 24 successive trials. For MM conditions, participants wore a pair of glasses that blocked their peripheral vision and for PV conditions they were asked to keep their focus on the vehicle in front throughout. Results showed that task time gradually decreased for the trials when participants could only use MM. However, overall task time for MM conditions were significantly higher than for those in which PV was utilised, and participants rated the use of MM to be more difficult than PV. In contrast, results suggest that for interfaces that utilise peripheral visual processing the learning effect is not evident and operation times are constant over time. These findings indicate that in-vehicle touch screens should be designed to utilise peripheral vision for making simple button selections with reduced visual demand

Methods for Multiloop Identification of Visual and Neuromuscular Pilot Responses

In this paper, identification methods are proposed to estimate the neuromuscular and visual responses of a multiloop pilot model. A conventional and widely used technique for simultaneous identification of the neuromuscular and visual systems makes use of cross-spectral density estimates. This paper shows that this technique requires a specific noninterference hypothesis, often implicitly assumed, that may be difficult to meet during actual experimental designs. A mathematical justification of the necessity of the noninterference hypothesis is given. Furthermore, two methods are proposed that do not have the same limitations. The first method is based on autoregressive models with exogenous inputs, whereas the second one combines cross-spectral estimators with interpolation in the frequency domain. The two identification methods are validated by offline simulations and contrasted to the classic method. The results reveal that the classic method fails when the noninterference hypothesis is not fulfilled; on the contrary, the two proposed techniques give reliable estimates. Finally, the three identification methods are applied to experimental data from a closed-loop control task with pilots. The two proposed techniques give comparable estimates, different from those obtained by the classic method. The differences match those found with the simulations. Thus, the two identification methods provide a good alternative to the classic method and make it possible to simultaneously estimate human's neuromuscular and visual responses in cases where the classic method fails

Usability of TeleFOT Nomadic and Aftermarket Devices [D1.8]

This deliverable reports on the Usability activities undertaken in TeleFOT mainly within WPs 4.8 and 4.10. These planned to support the Sub-Project 4 of TeleFOT in Evaluation and Assessment of nomadic devices within the national Field Operational Tests (FOTs). The key objective of WP4.8 in this regard is to provide measurable data that allows comparing usability and user experience of different driver assistance services whilst the key objective of WP4.10 is to identify and define the target and actual technical performance metrics for the Nomadic Devices (NDs) used. Two approaches are described in this Deliverable which have been utilised within TeleFOT for evaluating the usability of the nomadic and aftermarket devices tested within the TeleFOT FOTs. The first approach describes the feedback received from the TeleFOT participants with regard to their user experiences with the devices tested during the FOTs. To complement this information, each test site was asked to supply usability information specifically related to the time taken and the number of user interactions (aka button presses) to access certain functions within their ND. These included time and interactions to access the main menu and primary function, or adjust the volume, as well as to start up and shut down. The participants’ opinions on the design of the device, user interface, initial reactions and benefits to the NDs were then recorded as were ‘Other Issues’ which related to participants’ perceived usefulness, reliability and ease to interpret the information offered by the ND. This method allowed in-depth information to be captured surrounding issues which may have influenced the use of the ND during the FOT and/or common issues which arose. The second approach involved expert evaluations undertaken by HMI analysts working at the test-sites on a number of devices that were tested within TeleFOT. Not all of the devices that were tested within TeleFOT were subjected to expert evaluations. However, the procedure for such evaluations is described along with the results