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
1.1. The Context and the Problem
1.2. Our Answer: The Concept of the Adaptive Co-Pilot
1.3. Contributions of the Work
- Development (Section 2): Three components of the adaptive co-pilot are presented in this work: (1) a novel lateral shared controller, able to assist the driver with different levels of haptic authority, (2) an arbitration module that calculates the authority to be assigned to the controller, designed under two principles (minimal intervention and safety-over-comfort), and (3) an innovative visual HMI for shared control, as the enabler of trust in decisions and actions performed by the co-pilot.
- Testing (Section 3.1): The implementation of the adaptive co-pilot is then tested in a scenario where the driver is asked to repeatedly perform a secondary task, which results in two general driver states (attentive or distracted). Under this scenario, four driving modes are compared (manual as a baseline, and LK and LC as two available commercial ADAS, and the proposed adaptive co-pilot). The last three offer steering support to the driver to keep within the lane in both attentive and distracted states. Tests were carried out in a driver-in-the-loop simulator with five drivers.
- Evaluation (Section 3.2 and Section 3.3): A quantitative analysis of the tests was performed, considering KPIs related to tracking performance, safety indicators, and driving efforts. To complement the study, a subjective evaluation was also performed through questionnaires applied to the participants, with KPIs related to safety, comfort, and overall perception of the system. Results show that when the driver is continuously kept in the loop under the support of the adaptive co-pilot, it provides benefits in regards to the driving task that would help to avoid “automation irony” (as the driver has a sense of being responsible for driving even under automation support). At the same time, drivers benefit from the automation capabilities for intervening during the distraction events and improving driving safety, while reducing driver-required efforts.
2. Materials and Methods
2.1. Adaptive Co-Pilot—Shared Controller
- The control signal (controller output) is the steering wheel torque, whereas conventional autopilots use the steering wheel angle  or even the angular velocity . However, drivers control the steering wheel by applying torque with the arms-hands mechanism, and it has been shown that steering angle control decreases the ease of driver intervention (as it has to deal with the low lever position controller) . Therefore, to couple the control signals of both agents, a torque-based lateral controller is designed, similar to previous works in lateral shared control that have followed the same approach .
- The authority of the controller is adaptative to the ever-changing environment and context, whereas conventional autopilots assign a single authority value to the controller, being either activated (authority = 1, or maximum torque applied) or deactivated (authority = 0). In autopilots, the context may change the behavioral planner, but the core controller remains with the same authority, whereas for the driver–automation “team”, the automation support can adapt its intensity according to the conditions of each scenario (changes in driver fit-to-drive conditions, unsafe driver actions, automation failures, and others). This means automation can assist the driver with different levels of intensity (authority), covering the continuous spectrum from “no assistance” to “maximum allowed assistance”. In the literature, this intensity is known as the level of haptic authority [22,29].
- The control method is able to perform optimization of multiple objectives, as the complex interaction between driver and automation creates a series of goals such as tracking performance, driver comfort, driving efforts, and safety, which cannot always be achieved at the same time. Therefore, a controller capable of balancing those objectives efficiently is ideal for the adaptive co-pilot. Whereas classical controllers such as PIDs have been widely used for autopilots, shared control applications are more benefited by optimal control algorithms that allow minimizing functions with multiple objectives, with the additional benefit of managing constraints of vehicle dynamic states and control signals .
2.1.1. The Vehicle Model
2.1.2. The Optimization Problem
2.1.3. The Adaptive Authority
2.1.4. The Stability Criteria
2.1.5. The Authority Dimension
2.2. Adaptive Co-Pilot—Arbitration
- The minimal intervention principle follows the idea that drivers only need assistance under specific circumstances; on the contrary, automation could create unnecessary conflicts that will produce a feeling for the driver of being controlled all the time, and decrease the driver acceptance rate. Additionally, not intervening when drivers are in suitable conditions to drive aims to increase the sense of responsibility for the driving task, in order to avoid over-trust in automation. Moreover, the inclusion of the lateral error in the logic is part of this principle, as assistance should not be given to the driver only considering the distraction level (as the driver could maintain a safe performance even under some levels of distraction). Therefore, a combination of performance (lateral error) and driver state (distraction level) is needed.
- The safety over comfort principle is based on the fact that safety has a higher priority over the parameters that add comfort to the driving task. In this sense, if the driver is not in a condition to drive, and is also performing an unsafe action, then the system has to intervene even if it overrides the driver or the maneuver creates discomfort (e.g., high lateral acceleration or strong torque at the steering wheel).
2.3. Visual HMI
3.1. Driving Performance Test
3.1.1. User Story
3.1.2. Experimental Conditions
3.1.3. Key Performance Indicators
- Tracking errors: the ability to follow the expected trajectory is evaluated by the RMS and maximum (MAX) lateral and angular tracking errors.
- Safety indicators: the RMS and minimum (MIN) TLC indicate how close (in time) the vehicle is from departing the lane limits. It is estimated at any given time assuming the steering angular speed would remain unchanged. Additionally, the percentage of the time driven with the TLC below a threshold of 3.8 s (the minimum TLC when the automated system drives alone by the testing route, with the configuration of the nominal controller) provides a measure of the risk exposure.
- Driving efforts: the RMS and maximum torque exerted by the driver provide a measure of the effort applied, which relates to conflict and comfort. The RMS and maximum automation torque provide a measure of the work performed by the system and are related to its efficiency.
- Safety-related indicators: evaluate the driver’s sense of being protected, being allowed to perform the secondary task, or being required to continuously monitor the environment.
- Comfort-related indicators: evaluate the driver’s feeling of the system being harmonious or too intrusive in its interaction.
- Overall driver perception: evaluates the driver’s assessment of the system, using its own criteria to ponder safety and comfort behavior.
3.2. Quantitative Evaluation (Experimental Results)
3.3. Qualitative Evaluation (Questionnaires)
- Did you have the feeling that you were free to perform the secondary task?
- Did you feel the system required your continuous monitoring of the situation?
- Did you have the feeling that the system was too intrusive?
- Did you feel that your security was ensured by the system?
- Did you feel that your interaction with the system was harmonious?
- Provide an overall evaluation of the system.
- Extend the experiments presented in this work, with a larger number of participants, a more realistic scenario with higher curvature sections, other secondary tasks, and additional metrics (e.g., secondary task fulfillment evaluation and take-over performance when automation support is unexpectedly deactivated). In particular, the take-over behavior will be a key aspect in the acceptance of the SC mode in the future, as it is expected to result in better performance in comparison to when the driver is kept out of the loop.
- Extend the scenarios beyond the distracted driver, using the driving simulator again. With reference to the work of Okada and colleagues , we will include curves and other road layouts. Moreover, two additional use-cases will be evaluated: (1) support in collision avoidance of sudden obstacles when driving in automated mode, and (2) support in an overtaking maneuver, evaluating transitions from automated to manual, initiated by the driver when the AD cannot perform this maneuver due to—for example—limitations in perception. In such a situation, the system informs the driver and asks for support. Two possibilities can be investigated: cooperation in perception (where the influence of visual HMI presented in this work will be evaluated) and real-time cooperation in action.
- Integration of the HMI: while presented in this work as an initial prototype, the next step is to integrate the visual HMI with real data coming from the simulator, to be used in the experimental test to evaluate its effectiveness as an enabler of trust in decisions and actions performed by the co-pilot.
- Implementation of the co-pilot enabler in a real demonstrator vehicle, using an automated Renault Twizy.
Institutional Review Board Statement
Informed Consent Statement
Data Availability Statement
Conflicts of Interest
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|ADAS||Advanced Driver Assistance System||LoA||Level of Automation|
|ADS||Automated Driving System||MF||Membership Function|
|AI||Artificial Intelligence||MPC||Model Predictive Control|
|AV||Automated Vehicle||NMPC||Non-linear MPC|
|FIS||Fuzzy Inference System||ODD||Operational Design Domain|
|HMI||Human–Machine Interface||RMS||Root Mean Square|
|ITS||Intelligent Transportation Systems||SAE||Society of Automotive Engineers|
|KPI||Key Performance Indicator||SC||Shared Control|
|LC||Lane-Centering||TLC||Time to Lane Crossing|
|State Vector||Prediction Model Function||Optimization Function||Constraints||Equation|
|Algebraic Expressions for Tire Model|
|Lat. Force front||(7)|
|Lat. Force rear||(8)|
|Steering Wheel Model|
Control Input||Input Model|
|Optimization Function ||Constraint||Equation|
|Control torque (u)||(Effort)||(13)|
|Control torque rate of change ()||(Effort)||(14)|
|Vehicle mass||1650 kg||Motor inertia||0.1 kg·m2||Cornering stiffness|
|940 × 102 N|
118 × 103 N
|Vehicle inertia||3234 kg·m2||Motor damping||0.65 N·s/rad||MPC horizon (N)||30|
|Distance to axis|
|Steering ratio||8.77||MPC sample-time ()||0.05 s|
|Variable||N° of MFs||[Labels] → [Values]||MFs|
|4||NONE = [−1.5 −0.57 −0.04 0.33]|
LOW = [−3.5 −0.01 0.32 1.04]
MED = [0.34 1.15 1.52]
HIGH = [1.04 1.54 2.54 3.04]
|3||LOW = [−0.53 −0.21 −0.01 0.87]|
MED = [0.26 0.68 0.91]
HIGH = [0.63 0.94 1.29 1.54]
|Authority||4||MAN = [−1 0 0.5 2]|
LOW = [0.5 2 6]
MED = [2.02 6.02 10]
HIGH = [14.3 14.8 24.3 24.8]
|Input ()||Input ()||Output ()||Design Strategy|
|MEDIUM||LOW||Safety + Comfort|
|HIGH||MEDIUM||Safety + Comfort|
|MEDIUM||MEDIUM||Safety + Comfort|
|HIGH||HIGH||Safety over Comfort|
|LOW||MEDIUM||Safety + Comfort|
|MEDIUM||HIGH||Safety over Comfort|
|HIGH||HIGH||Safety over Comfort|
|Manual Driving (MANUAL)||Lane Keeping|
|No torque is applied by the automation. The driver only feels the self-aligning torque.||When the vehicle approaches the border line, the vehicle intervenes, applying a momentary torque.||A continuous torque is applied to keep the vehicle on a reference trajectory within the lane.||If the driver is attentive, they receive minimal correction torque, which only increases when getting close to the borders. If the driver is distracted, the authority increases and the free moving range is reduced.|
| Nm|| Nm|
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Marcano, M.; Tango, F.; Sarabia, J.; Castellano, A.; Pérez, J.; Irigoyen, E.; Díaz, S. 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. Appl. Sci. 2021, 11, 6950. https://doi.org/10.3390/app11156950
Marcano M, Tango F, Sarabia J, Castellano A, Pérez J, Irigoyen E, Díaz S. 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. Applied Sciences. 2021; 11(15):6950. https://doi.org/10.3390/app11156950Chicago/Turabian Style
Marcano, Mauricio, Fabio Tango, Joseba Sarabia, Andrea Castellano, Joshué Pérez, Eloy Irigoyen, and Sergio Díaz. 2021. "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" Applied Sciences 11, no. 15: 6950. https://doi.org/10.3390/app11156950