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Purpose: Alcohol is the most frequent detected substance in the automobile fatal crashes but its precise mode of action on mental state is not always clear, notably when combined with lack of driving experience. The aim of this work is thus to evaluate performance and self-reported alertness and effort as a function of low doses of Blood Alcohol Concentration (BAC) and of drivers' experience. Methods: A single blind counterbalanced experiment was conducted on a driving simulator. Fifteen young novice drivers (18 years old, less of 2 months of driving licence) and fifteen young experienced drivers (21 years old, 3 years of driving licence) were tested with 0.0 g/l, 0.2 g/l and 0.5 g/l of alcohol at early afternoon. Driving scenarios was road tracking during 45 min at a constant speed of 110 km/h while maintaining a steady trajectory. Participants responded to the Thayer's scale before and after each driving session, and to an adaptation of the NASA-TLX after each driving session. Statistical analyses were performed on driving parameters, self-reported alertness and effort as a function of alcohol level and driving experience. Results: Alcohol deteriorated lateral and longitudinal stability of the trajectories. Notably with BAC 0.5 g/l, effort and speed increased but the increase in speed was particularly high for young experienced drivers. When young novice drivers felt that they were low alert, they also felt that they exerted more effort, their performance decreased, but they reduced their speed. Conclusions: Alcohol thus degrades driving performance, and especially when the effort is high and alertness is low. The decrease of inhibitory process noted in presence of alcohol seems thus higher for young experienced than for young novice drivers. Results are in line with a drop in the blood alcohol level tolerated while driving.

Background Surging acceptance of adaptive cruise control (ACC) across the globe is further escalating concerns over its energy impact. Two questions have directed much of this project: how to distinguish ACC driving behaviour from that of the human driver and how to identify the ACC energy impact. As opposed to simulations or test-track experiments as described in previous studies, this work is unique because it was performed in real-world car-following scenarios with a variety of vehicle specifications, propulsion systems, drivers, and road and traffic conditions. Methods Tractive energy consumption serves as the energy impact indicator, ruling out the effect of the propulsion system. To further isolate the driving behaviour as the only possible contributor to tractive energy differences, two techniques are offered to normalize heterogeneous vehicle specifications and road and traffic conditions. Finally, ACC driving behaviour is compared with that of the human driver from transient and statistical perspectives. Its impact on tractive energy consumption is then evaluated from individual and platoon perspectives. Results Our data suggest that unlike human drivers, ACC followers lead to string instability. Their inability to absorb the speed overshoots may partly be explained by their high responsiveness from a control theory perspective. Statistical results might imply the followers in the automated or mixed traffic flow generally perform worse in reproducing the driving style of the preceding vehicle. On the individual level, ACC followers have tractive energy consumption 2.7 – 20.5 % higher than those of human counterparts. On the platoon level, the tractive energy values of ACC followers tend to consecutively increase (11.2 – 17.3 %). Conclusions In general, therefore, ACC impacts negatively on tractive energy efficiency. This research provides a feasible path for evaluating the energy impact of ACC in real-world applications. Moreover, the findings have significant implications for ACC safety design when handling the stability-responsiveness trade-off.

This study aimed at investigating how driver's mental workload could be assessed during driving, using driving performance as well as electrophysiological and subjective data. Participants had to follow a lead vehicle at a safe and constant distance and to deal with two particular driving events (overtaking and pedestrian occurrence) within two sessions (baseline and experimental) on a driving simulator. Traffic density and time pressure (overtaking event) and time pressure (pedestrian event) were increased in the experimental session in order to induce a higher workload. Participants filled NASA TLX questionnaire after each driving session. Electrophysiological parameters (SCL, ECG), driving performance (SDLP and response to speed change of the lead vehicle: coherence, delay and gain) were analysed after each event in two temporal windows (30 sec and 5 min). Results showed that both performance and physiological variables differed as a function of traffic conditions and time pressure. Moreover, while performance variations were systematically observed over a long period (five minutes after the events), effects on mean SCL data obtained from experimental session notably differed from baseline values within thirty seconds after the events. Results are discussed in term of mental workload and suggestions are made about the safety systems that could monitor driver's mental state.