- The paper introduces an iterative, location-specific Speed Profile Adjustment Algorithm that averages smoothed driver intervention profiles with a predictive longitudinal driving baseline rather than copying corrections directly.
- In a 43-participant simulator study, adaptive driving increased general satisfaction from 3.69 to 4.27, improved speed-profile satisfaction from 3.06 to 4.14, and reduced interventions from 54.68% to 22.97%.
- The findings show that intervention-based personalization can improve assisted driving, but deployment should classify intentional feedback, accumulate evidence across drives, communicate learned speeds clearly, and validate performance in real vehicles over longer periods.
Motivation and research gap
Voluntary driver-initiated takeovers within the operational design domain (ODD) of assisted driving functions are common in real-world driving, and prior work by the same group established that most such interventions are preference-driven adjustments in free-driving scenarios, with intervention frequency negatively correlated with stated driver satisfaction. Notably, drivers intervene consistently only at specific locations rather than uniformly across comparable road segments, motivating a location-based rather than global personalization strategy.
The paper identifies three gaps in the existing literature on ADAS personalization. First, prevailing approaches are one-shot driver models trained on recorded manual driving, yet the literature is inconclusive on whether drivers actually prefer their manual driving style as the automated style—some studies report a preference for self-similar styles, others for more defensive automated behavior. Second, these models are almost exclusively evaluated by imitation fidelity of manual driving rather than by their effect on satisfaction during active system use. Third, related work concentrates on ACC car-following parameters, whereas interventions occur predominantly in free-driving scenarios. The proposed remedy is a continuously updating, location-specific speed profile adjustment driven by implicit feedback collected while the driving function is active—an interactive, traded-control setting analogous to Interactive Imitation Learning (IIL), but without learning a general policy.
Speed Profile Adjustment Algorithm
The core contribution is the Speed Profile Adjustment Algorithm (SPAA), applied to an SAE level 1 Predictive Longitudinal Driving Function (PLDF) that respects legal speed limits and computes curvature-based curve speeds. Drivers intervene via pedal presses or set-speed offsets; the base PLDF does not retain any of these adjustments across drives.
For pedal interventions, the SPAA deliberately avoids replaying the demonstration verbatim, for two reasons: the underlying driver intention cannot be recovered exactly from the intervention trajectory, and human corrections are delayed and overreactive—a well-documented issue in IIL frameworks such as Expert Intervention Learning and hierarchical safe-learning approaches. The algorithm therefore preprocesses each intervention by:
- Temporal stretching: the intervention's speed-over-distance segment is stretched backwards by a stretch factor α=0.5 (chosen qualitatively from pre-studies), capped so that no point moves more than the distance corresponding to 3 seconds, and reduced near high-curvature segments for safety. This ensures that the "bad states" that triggered the intervention are overwritten.
- Offset alignment: a linearly decaying offset aligns the stretched profile's start velocity with the surrounding driver-driven profile, removing discontinuities.
- Averaging and smoothing: the mean of the PLDF baseline profile vPLDF(d) and the preprocessed demonstration vprepro(d) is computed and smoothed with a second-order Savitzky–Golay filter. This averaging compensates for overreactive human behavior, yielding a profile smoother than the demonstration but closer to driver preferences than the baseline.
Set-speed interventions are adopted directly, except when executed shortly after a speed limit change, where the offset is extended over the whole segment. The procedure is iterative: after each drive, the adjusted profile becomes the new baseline, and drivers can continue refining it through further interventions. In this study, all interventions were treated as voluntary because the simulated route contained no ODD-exit scenarios; the authors note that automatic intervention classification would be required in deployment.
Study design
The evaluation was a simulator-based test group study with 43 valid participants, all regular ADAS users, conducted in a hexapod motion simulator with LED walls and a production-equivalent PLDF mockup. The track was a 4.5 km curvy rural road (roughly 3.5 minutes per drive) with speed limits between 50 and 100 km/h. Each participant drove two familiarization runs, then experienced system A (base PLDF) and system B (adaptive PLDF), each consisting of two drives with interventions followed by one drive without interventions to experience the final profile. System B was pre-trained on the interventions from the second system A drive and updated after every subsequent drive. Satisfaction was measured with the acceptance scale of van der Laan et al. plus a custom six-item Likert questionnaire targeting specific speed-profile aspects derived from previously observed intervention types.
The authors explicitly acknowledge a design limitation: the A-then-B order was fixed because B requires A's intervention data, making the study susceptible to learning effects; randomization was deemed impractical within the one-hour session budget chosen to limit fatigue and motion sickness.
Quantitative results
Both hypotheses were confirmed with strong statistical support:
| Measure |
System A |
System B |
Test |
| General satisfaction (1–5) |
3.69 |
4.27 |
paired t-test, t(42)=4.60, p=3.85×10−5 |
| Speed-profile satisfaction (1–5) |
3.06 |
4.14 |
Wilcoxon signed-rank, W=45.0, p=3.83×10−9 |
| Combined intervention rate |
54.68% |
22.97% |
Wilcoxon signed-rank, W=1, p=4.55×10−13 |
| Pedal IR |
22.32% |
12.04% |
paired t-test, t(42)=6.42, vPLDF(d)0 |
| Set-speed IR |
39.76% |
12.42% |
Wilcoxon signed-rank, vPLDF(d)1, vPLDF(d)2 |
Thirty-four of 43 participants rated system B higher on general satisfaction (six rated it worse, examined below), and 40 of 43 preferred its speed profile. The set-speed IR showed the largest relative reduction, which follows directly from the SPAA persisting set-speed offsets across iterations—behavior the base PLDF lacks entirely. An example driver's profile development shows that most segments required only a single iteration, with repeated refinement confined to a few locations (e.g., after speed limit increases). Importantly, IRs did not converge to zero after two iterations, leaving open whether longer-term use would eliminate interventions entirely.
Qualitative feedback and identified weaknesses
Free-form feedback was largely positive, with participants describing system B's driving style as more natural and human-like compared to the "machine-like" base system. Two recurring criticisms carry direct design implications. First, insufficient communication: because the learned profile may deviate from both the legal limit and the displayed set speed, drivers wanted explicit HMI feedback (e.g., cluster icons or text) explaining the current target speed, and felt the learning-from-interventions mechanism should be disclosed to customers. Several participants who rated system B's speed profile higher nonetheless assigned higher general satisfaction to system A solely due to this opacity—the clearest divergence between the two satisfaction measures in the study. Second, unfiltered learning: every intervention, including unintentional ones, was learned immediately without arbitration, and affected drivers found incorrectly learned behavior irritating to overwrite. The authors argue that deployment requires neural-network-based classification of intervention intent combined with accumulation over multiple drives to identify consistent intervention hotspots before applying the SPAA.
Limitations and open questions
Beyond the fixed experiment order and the absence of intervention arbitration, the study's one-hour duration precluded assessment of long-term adaptation dynamics, including whether IRs converge to zero or whether repeated averaging could cause drift or oscillation in the learned profiles. All results were obtained in simulation on a single 4.5 km route with a homogeneous participant pool recruited internally, so simulation-to-reality transfer and generalization across route types remain untested. The stretch factor vPLDF(d)3 was selected qualitatively rather than optimized, and the safety-motivated reduction of stretching in high-curvature segments was not separately evaluated. Finally, the interaction between the SPAA and traffic scenarios (car-following, ODD exits) was excluded by design.
Conclusion
This paper demonstrates that iterative, location-based speed profile personalization from implicit feedback—driver interventions during active use of a level 1 PLDF—significantly increases driver satisfaction and significantly reduces intervention frequency in a 43-participant simulator study, addressing the gap left by one-shot manual-driving imitation approaches whose benefit to actual users had not been evaluated. The results validate interventions as a usable personalization signal, provided they are classified and accumulated before learning. The remaining questions are concrete: long-term convergence of intervention rates, randomized-order replication, integration of intervention arbitration, real-vehicle validation, and an HMI concept that makes the learned behavior transparent to the driver.