Truck Drivers' Behaviors and Rational Driving Assistance
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- Muriel Moore
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1 Truck Drivers' Behaviors and Rational Driving Assistance AE Project 1326 Annick Maincent Consultant in Cognitive Ergonomics Ph. D. student in Cognitive Psychology Laboratory for Studies and Analysis of Cognition and Models (LEACM) University Lyon 2 / Auxirbat Recherche 1
2 Project Organization Field of research: Human behaviors during truck driving activity in real and/or simulated situations Three main parts: 3 studies about economical and psycho-sociological factors in relation to rational driving and truck driver's job (separately report) A Ph D thesis in cognitive psychology about modeling truck drivers' behaviors in relation to a rational driving A Master of research in cognitive psychology to assess a new HMI: an haptic accelerator pedal (separately report) 2
3 Goal of Renault Trucks' Advanced Engineering To develop an adaptive eco-driving assistance system in order to help the driver to optimise his fuel consumption according to the best strategy Taking into account and integrating, in real time, driving styles, drivers' behavioural variability and data coming from the road environment through the Electronic Control Unit To design a new HMI based on force feed-back in accelerator pedal 3
4 PhD Thesis Approach "Research about interactions between technical progress and human behaviors with the idea of sustainable development: about the economical driving assistance of a truck" Focus on economical, psychological and cognitive aspects of truck drivers' behaviors in order to a rational driving Systemic approach of truck driving activity with the driver in the heart of the system Driver/Vehicle/Environment Pluri-disciplinary approach: Human Science are applied to Advanced Engineering from a behavioral point of view Multi dimensional methodology 4
5 Systemic Approach DRIVER Action Information ENVIRONMENT H.M.I. Human Machine Interface VEHICLE ASSISTANCE 5
6 Goal of Thesis Researches Providing Renault Trucks with a correct truck drivers' behavior model in relation to a rational driving Translation of driving behaviors into objective and measurable criteria Identification of bad behaviors that an assistance system can correct in order to decrease fuel consumption Designing behavioural models according to personality, cognitive and driving styles Validation of a workload index for truck driving activity 6
7 Rational Driving Optimisation of the global vehicle productivity : Optimisation of drivers' fuel consumption Optimisation of the use of the drive line components (braking, gear changes, use of engine speed, etc.) Optimisation of the ratio "delays for delivery/fuel consumption" Economy + Safety + Driving comfort 7
8 Experimental Plan 33 professional truck drivers Vehicle (38 T): Tractor Renault Premium Long Distance 420 dci+ trailer Route = 170 km/105,65 Miles (alt= m) divided in 2 sections: Route A = 92 km/57,17 Miles, highways and urban areas, Route B = 78 km/48,48 Miles, motorways Experimental procedure (+/- 4h30): driving (+/- 3h) & various questionnaires (+/- 1h30) Vehicle data recording in real time acquisition on a PC file Road environment data recording with observation grids 8
9 Experimental System Cognitive and conative factors Knowledge Workload Experience Questionnaires, Cognitive + Personality tests, Representations Workload Index Driving Behaviors Driver Behaviour Analysis Vector CANalyser Actopalm 9
10 Workload Driver Index (NASA TLX) DRIVING TASK Mental Demand Physical Demand Temporal Demand DRIVER Subjective Workload Effort Frustration Own Performance Workload Index Overall Score Mental Demand Physical Demand Temporal Demand Effort Frustration Performance 10
11 Results: Drivers' Workload Workload factors analysis according to the route (n = 33) 60 route A route B Route A = Varied roads Route B = Motorways 0 Mental Demand Physical Demand Temporal Demand Effort Frustration Loading factors Performance Overall Score Varied roads Overall Workload > Motorways OW Focus on mental & physical demands, effort & frustration on varied roads 11
12 8,0 6,0 Results: Driving Behaviors Comparison of consumption and speed, by driver, on the route A (all roads) Rational Behaviors Worst Behaviors 4,0 Gaps value 2,0 0,0-2,0-4,0-6,0-8,0 T3 T1 S14 S12 S22 S13 S25 S20 S23 S11 Drivers n= 33 S3 S24 S6 S10 S8 S1 T2 S26 Consumption S29 S27 S17 S16 S7 S2 S21 S30 Speed S28 S19 S5 S9 S18 S15 S4 Great inter-individual variability: Consumption and Speed Dissociation Consumption/Speed 12
13 Results: Psychological & Cognitive Factors No relation has been found between drivers' consumption and drivers' personality or cognitive styles Stress resistance of tested drivers very resistant: 1 driver resistant: 9 drivers Fairly resistant: 21 drivers Slightly resistant: 3 drivers Not at all resistant: 0 Studies (Renault + INSA Lyon) have shown that it is preferable for a driver to be stress resistant or fairly resistant than very, slightly or not resistant There is no difference between our drivers' pattern of personality and the male's average pattern (EPI sources) 13
14 Conclusions (1) Dissociation consumption/speed = 1. It is possible to reduce the fuel truck consumption without wasting time Drivers' consumption only depends on their own driving style (acceleration, braking, anticipation) = 2. It is not possible to select truck drivers from their personality or cognitive style Fuel consumption mainly depends on drivers' cognitive acquired automatisms = 3. Importance of first driving learning and rational driving trainings 14
15 Conclusions (2) One of the better solutions for reducing fuel consumption whoever drives the truck is to develop an adaptive assistance system for helping "bad" drivers without bothering "good" drivers especially on varied roads Do not overlook driving learning and trainings When the new system will be developed, make sure that it does not increase the drivers' workload Be careful of the Human Machine Interface 15
16 Interest Adaptation of a workload index for evaluating drivers' workload and failings of attention and vigilance induced by different technical systems (HMI, driving assistances, displays...) Design of a specific software for collecting and processing data by Vehicle ECU Updating of a database (signalisation, infrastructures...) for a simulation tool (Geode) Development of a new professional expertise in the firm The Psycho-Cognitive Ergonomics 16
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