A key question in transportation research is whether drivers show behavioral adaptation, that is, slower or faster driving, when new technology is introduced into the vehicle. This study investigates behavioral adaptation in response to the sport mode, a technology that alters the vehicle's auditory, throttle-mapping, power-steering, and chassis settings. Based on the literature, it can be hypothesized that the sport mode increases perceived sportiness and encourages faster driving. Oppositely, the sport mode may increase drivers’ perceived danger, homeostatically causing them to drive more slowly. These hypotheses were tested using an instrumented vehicle on a test track. Thirty-one drivers were asked to drive as they normally would with different sport mode settings: Baseline, Modified Throttle Mapping (MTM), Artificial Engine Sound enhancement (AESe), MTM and AESe combined (MTM-AESe), and MTM, AESe combined with four-wheel steering, increased damping, and decreased power steering (MTM-AESe-4WS). Post-trial questionnaires showed increased perceived sportiness but no differences in perceived danger for the three MTM conditions compared to Baseline. Furthermore, compared to Baseline, MTM led to higher vehicle accelerations and, with a smaller effect size, a higher time-percentage of driving above the 110 km/h speed limit, but not higher cornering speeds. The AESe condition did not significantly affect perceived sportiness, perceived danger, and driving speed compared to Baseline. These findings suggest that behavioral adaptation is a functional and opportunistic phenomenon rather than mediated by perceived sportiness or perceived danger.

Introduction: Cars are increasingly computerized, and vehicle settings such as steering gain (SG) can now be altered during driving. However, it is unknown whether transitions in SG should be adaptable (i.e., triggered by driver input) or adaptive (i.e., triggered automatically). We examined this question for road segments expected to require different SG. Objective: This paper aimed to investigate whether SG mode changes should be made by the driver or automatically. Methods: Twenty-four participants drove under four conditions in a simulator: fixed low gain (FL), fixed high gain (FH), a machine-initiated steering system, which switched between the two SG levels at predetermined locations (MI), and a driver-initiated steering system, in which the SG level could be changed by pressing a button on the steering wheel (DI). Results: Participants showed poorer lane-keeping and reported higher effort for FH compared to FL on straights, while the opposite held true on curved roads. On curved roads, the MI condition yielded better lane-keeping and lower subjective effort than the DI condition. However, a substantial portion of the drivers gave low preference rankings to the MI system. Conclusion: Drivers prefer and benefit from a steering system with a variable rather than fixed gain. Furthermore, although automatic SG transitions reduce effort, some drivers reject this concept. Application: As the state of technology advances, MI transitions are becoming increasingly feasible, but whether drivers would want to delegate their decision-making authority to a machine remains a moot point.

Current predictors of fuel consumption are typically based on computer simulations or data collections in real traffic, where the route and vehicle type are not under the researcher's control. Here, we predicted fuel consumption using test track data, an approach that allowed for location-specific predictions. Ninety-one drivers drove a total of 4617 laps, in two vehicles (Renault Mégane, Renault Clio), on two routes (highway and mountain), and with two eco-driving instructions (normal and eco). A multivariate analysis at the level of laps showed a strong predictive value for metrics related to speed, RPM, and throttle position, but with a considerable amount of variance attributable to route and vehicle type. A subsequent location-specific analysis showed that the predictive correlation of driving speed and throttle position fluctuated strongly during the lap and at some locations even became negative. We conclude that there is considerable potential in instantaneous location-specific prediction of fuel consumption.

When drivers encounter a road narrowing two potential adaptation strategies come into play that may increase safety margins: decreasing speed and increasing neuromuscular stiffness of the arms. These two adaption strategies have so far been studied in isolation. We expect that there is a trade-off between these two strategies, and that risk duration would impact a driver's selection of the trade-off. Specifically, we hypothesized that for a short risk duration, drivers will favour increased neuromuscular stiffness over speed reduction; and vice versa for longer risk durations. Twenty-six participants drove in a driving simulator and encountered different risk durations; realized by road narrowings (from 3.6 m to 2.2 m) of varying lengths (10 m, 100 m, 250 m, and 500 m). The neuromuscular stiffness was quantified by measuring the grip force exerted by both hands. The results show that all road narrowing conditions successfully induced driver adaptations, as a significant reduction in speed and increase in grip force was observed. However, the tested drivers did not consistently select the hypothesized different trade-offs for increasing duration of road narrowing: a low correlation was found between speed and grip force adaptations. Interestingly, individual trade-off were consistent: the within-subject variability in speed-grip force adaptations was low across the tested risk durations. Future research should further elucidate the underlying motivations for these individual adaptation strategies.

Several modern vehicles provide the option to select a driving mode. However, the literature contains no empirical studies that investigate how driving modes affect the vehicle's dynamic behaviour in regular on-road driving. We examined for which CAN-bus signals the differences between Renault's Multi-Sense® comfort and sport modes are most apparent. We gathered data on a 26.3 km route containing a rural and highway section. A single person drove the route four times in comfort mode and four times in sport mode. By statistically analysing and ordering 887 CAN-bus signals, we found strong differences between the two modes for rear-wheel angle, engine torque, longitudinal acceleration, and vertical motion. Parameter identification of a quarter car model identified a 3.5 times higher damping coefficient for the sport mode compared to the comfort mode. Due to four wheel steering, compared to the comfort mode, the sport mode yielded a higher lateral acceleration and yaw rate for a given steering wheel angle and driving speed. In conclusion, this study provides quantitative insight into the extent to which the Multi-Sense driving modes impact the vehicle's lateral, longitudinal, and vertical dynamic behaviour. The results and the analysis methods help guide future driving mode designs.

The present study aims to add to the literature on driver workload prediction using machine learning methods. The main aim is to develop workload prediction on a multi-level basis, rather than a binary high/low distinction as often found in literature. The presented approach relies on measures that can be obtained unobtrusively in the driving environment with off-the-shelf sensors, and on machine learning methods that can be implemented in low-power embedded systems. Two simulator studies were performed, one inducing workload using realistic driving conditions, and one inducing workload with a relatively demanding lane-keeping task. Individual and group-based machine learning models were trained on both datasets and evaluated. For the group-based models the generalizing capability, that is the performance when predicting data from previously unseen individuals, was also assessed. Results show that multi-level workload prediction on the individual and group level works well, achieving high correct rates and accuracy scores. Generalizing between individuals proved difficult using realistic driving conditions but worked well in the highly demanding lane-keeping task. Reasons for this discrepancy are discussed as well as future research directions.