Collective Data Sharing to Improve on Driving Efficiency and Safety Andreas Riener Johannes Kepler University Linz Institute for Pervasive Computing Altenberger Strasse 69, 4040 Linz
[email protected] Johann Reder Johannes Kepler University Linz Institute for Pervasive Computing Altenberger Strasse 69, 4040 Linz
[email protected]
Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from
[email protected]. AutomotiveUI ’14, September 17 - 19 2014, Seattle, WA, USA Copyright 2014 ACM 978-1-4503-0725-3/14/09. . . $15.00 http://dx.doi.org/10.1145/2667239.2667266
Abstract Traffic is a social system in which road users have their own personality and steer their cars based on learned behavior, experience, and familiarity with situations or street sections. The assumption of this work is, that traffic efficiency and safety could be enhanced when the more competent road users support the less competent ones by sharing data about specific road characteristics and providing steering recommendations. This information should help the latter to move its vehicle in a more efficient and safe way. We thus designed, prototyped, and tested a “Social driving app” that allows experienced drivers to collect and share driving data (speed, gear, brake force, etc.) and that generates, based on the aggregated driving profiles of experts, steering recommendations for the lay drivers. By introducing a ranking system to motivate the individual drivers to follow the instructions from the system, the project further examined the influence of social pressure on driving performance.
Author Keywords Driver-vehicle interaction; Social driving; Field operational test (FOT); Steering recommender system; NASA TLX.
ACM Classification Keywords Human-centered computing: [Human computer interaction (HCI)]: HCI design and evaluation methods, User studies
Socializing in Vehicle Operation It is a politically stipulated necessity to (further) improve on fuel economy, but, due to physical limits, efficiency improvements on the engine/power train side are almost exploited. There is, however, still room for enhancements based on individual usage patterns as simulations have recently confirmed [4]: Providing the driver with a track preview in the range of about 60sec. can save up to 33% of energy. This huge potential sounds good in theory, but in practice assistance systems have to deal with individual drivers with own personality and intentions, and good rationales are needed to motivate drivers to actually change their learned steering behavior. The approach followed in this work is two-fold. First, motivated by the fact that humans naturally tend to compete with each other, it proposes to use a “social companion” implementing a rewarding system to achieve the required behavior change on local level. We expect that drivers tend to follow instructions from an socialenabled assistance system to improve their position in a ranking list [7]. Second, in order to allow for improvements on the macro scale, it is on the aspect of “information collecting and sharing” in networks of cars. This is based on the fact that poor driving of just a single car could result in a traffic breakdown on large scale. By gathering and processing the traveling profile of experienced drivers familiar with a certain track, and finally sending recommendations for optimized vehicle steering (i. e., when to apply the brake or start to accelerate, which gear to engage, notification of danger spots such as a bus stop, blind bend, deer pass, speed trap, etc.) to other (in particular, novice/less experienced) drivers, we expect that the global driving performance will be improved as all the cars would travel more homogenized (i. e., with similar movement patterns). Further on, also road safety should be positively influenced as all the cars would benefit from an enhanced environmental perception and the similarity in motion patterns. A system implementing this
functionality is considered an important step towards the co-existence of driverless cars and manual driven vehicles. Ideal driving profiles should also be beneficial as initial parameterization of autonomous cars. Research hypotheses To show that social-inspired, connected driving techniques can indeed induce a behavior change in vehicle handling, we have implemented a system consisting of an Android app and a cloud-based back end and evaluated it in a small-scale field operational test (FOT). The user study was executed under realistic conditions (e. g., with oncoming traffic and cars running ahead) on a federal highway (route length 19.4km). Baseline data (for the later recommendations) was collected by drivers familiar with the test track, control group drivers without prior knowledge of the track had to complete a driving task without any recommendations, test group drivers received driving instructions or recommendations from a social app/companion while passing through the same route. In a first step, only driving efficiency in terms of fuel consumed was evaluated. • (H1) The average fuel consumption of the control group is higher than the fuel consumption of the baseline group, i. e., drivers familiar with a route drive more fuel-efficient compared to drivers unfamiliar with the route. • (H2) The fuel consumption of the test group is, on average, lower than that achieved by the control group (both groups unfamiliar with the route), i. e., the “social companion” providing steering recommendations helps to improve on fuel-efficiency.
Related work Lot of previous work has shown the ability to change driver behavior and to improve driving efficiency by integrating eco-driving systems into the car [1], using apps
Figure 1: The OpenXC interface (OBD-II connector, 10/100Mbit Ethernet, USB/Bluetooth) used to gather vehicle-specific data (amongst others, steering wheel angle (◦ ), torque (N m), engine speed (RP M ), vehicle speed (km/h), accelerator pedal position (%), fuel consumption (l), transmission gear position).
Driver UI Render recommendations in realtime
Vehicle Gather OBD-II data (OpenXC Bluetooth)
Android Device GPS Accelerometer
SOAP Web Service
MySQL Database
Figure 2: Main components of the “Social driving app”.
in car fleets [9], or applying subliminal techniques to induce a behavior change below active awareness [6]. In this work, we are following a different approach by hypothesizing that drivers knowing a route very well should know best how to drive along that route in an economic or optimal way irrespective of the current road situation or environmental conditions. This approach was first theoretically described in [7]. There are quite a few parameters that have to be taken into account when developing recommender systems aiming at improving on driving efficiency (i. e., fuel consumption). One part accounting for driving economy is directly related to the nature of driving (conservative vs. offensive) and manipulated by the individual driver (determined by the way a driver engages gears, using the brakes, and accelerates again after stops at crossings or at the end of curves). The other part is outside of direct influence by the driver and related to the overall traffic situation (stop-and-go traffic, jam, slow-moving truck ahead, etc.), environmental factors such as the weather condition (rain vs. sunshine, snow covered road, strong wind) or the physical characteristics of the vehicle (aerodynamics, condition of the wheels, vehicle weight [2]). In the course of this work, only factors from the first part are considered in the recommendation system; to avoid influence as much as possible of the second part, the driving study was conducted with one and the same car for all runs and at similar traffic and environmental conditions. Looking only on the three parameters (1) gears and (2) acceleration/deceleration in relation to the (3) driven track, this should give enough potential to significantly change fuel consumption based on our hypotheses. The engaged gear has a great influence on the fuel consumption as it is directly coupled with the engine RPM at a certain speed. As the internal friction losses of the engine are growing with engine speed, it is suggested to shift to a higher gear as soon as possible (as long as the
required torque is also provided by the lower RPM on the higher gear) [8]. Braking and acceleration actions depend much on the current traffic situation and road condition, i. e., variations in traffic congestion can create differing performance results even though the route is the same (not considered in this work), but unforesightful driving accounts for much more fuel consumption. Harsh braking uses more fuel and requires an increase in the number of gear changes, on the other side, forward planning and use of the momentum of the vehicle can save fuel (the speed gathered under power can be used to ascend and descend hills without using fuel) [3]. This is supported by previous research that has found out that fuel economy could be improved by up to 33% by giving the driver extended perception of the track he/she is driving on [5].
System Setup Figure 2 gives a coarse indication of the structure of the proposed system and its main components. Vehicle-specific data is gathered from the On-board Diagnostics (OBD) port via an ChipKIT Max32 OpenXC adapter (http://openxcplatform.com). This adapter connects via Bluetooth to a Smartphone hosting the OpenXC Android framework, translating the OBD-II signals from the vehicle into a standardized protocol of 18 signals (Figure 1). Driver feedback is provided in two ways, 1) visually using a Samsung Galaxy Tab mounted near the center console of the vehicle and providing recommendations for driving speed, gear to engage, and actual ranking (Figure 3), and 2) auditory information in the form of an “applause” sound if achieving rank 1 (corresponding to lowest fuel consumption for the current route segment) or a “puuuh...” sound if falling back to a certain configurable rank (e. g., rank 8 or worse). In order to not distract from the driving task, visual information was shown with care (e. g., large traffic sign indicating the recommended target speed), and updated rarely.
Distance
45
1
4 Ranking list
2
3
Gear cur: rec:
3 4
1. J. Doe 2. M. Muster 3. E. Bush 4. 5. L. Jackson 6. H. Ford 7. H.G. Wells 8. E. Jolie
Legend Distance bar: indication of remaining distance/time
1 before the next action (speed adaptation (2) or gear
change (3)) should be completed Traffic sign: shows the next traffic event that should be handled. Currently, only speed limit signs are 2 shown, but any other sign according to StVO, §48-54 (Austria) could be used here (e.g., „Children“, „Dangerous bend“) Gear recommender: Based on collective information, 3 the system suggests a gear to engage. Current gear is colored red if not matching the recommendation. Ranking list: Shows the current performance of the driver (e.g., fuel economy) compared to other drivers 4 driven on that route (database query). In addition, auditory information is provided for rank 1 ( “applause“) or if below a certain rank ( “puuuh...“)
Figure 3: User interface of the “Social driving app” as presented to the test group.
Vehicle location and orientation tracking is achieved by using the GPS (NMEA format) and accelerometer data from the Android device. Route information is extracted from an ‘OpenStreetMap’ map and fragmented into segments (depending on road structure, few ten or hundred meters) and the vehicle’s GPS coordinates are used to map the actual position to the track using nearest normal distance (projection of vectors) or dead reckoning on bad GPS signal. Depending on the mode (experienced or inexperienced driver) and in combination with the available vehicle-specific data, location-based driving information is finally provided to a web service (experienced driver) or steering recommendations are received from this service (novice driver). A SOAP web service manages 1) the data collected by the mobile devices of experienced users and 2) data exchange with inexperienced/novice users (push notifications on the UI/tablet computer). The service processes per-subject vehicle information (e. g., OpenXC/OBD data such as speed, pedal and gear positions, fuel consumption, etc.) for every route section. The Android device in the car maps real-time data to a location and transfers the data set to web service where it is further processed. Social driving app/“Companion” The Android app provides the user frontend and collects and analyzes vehicle-specific data (OpenXC). Furthermore, it connects to and exchanges data with the web server. The main components and functionality of the “Social driving app” are highlighted in Figure 3. A settings screen (not shown) allows to parameterize the app, for example to set user ID, configure database access, enable audio feedback, etc. Providing “hints” to inexperienced or novice drivers works quite simple. The app holds track data for about 1km in advance in its memory and checks if there are, based on the aggregated data of experienced drivers, upcoming events (speed or gear change, etc.). If so, the app
generates recommendations in form of information symbols as shown in Figure 3. Events that are very close are ignored in order to not distract the driver and cause incorrect responses or faulty steering actions.
Experiment design To check the research hypotheses, a small-scale user study was designed and executed as follows. The on-road driving study was carried out as between-subjects field operational test (FOT) with 9 participants (male only, aged 23-26 years), arranged in three equal-sized groups (baseline group: experienced drivers with expert knowledge of the selected route; control group: no assistance system/app; test group: driving recommendations via the “Social driving app”). None of the participants (except for the baseline group) were familiar with the route used in the study nor had previous knowledge about the aim of the experiment or the group they were assigned to. To guarantee equal test conditions, all participants used the same car (Ford Fiesta 2013 model) with low beam lights on and AC and car stereo off. Execution and data evaluation The study was carried out on a 19.4km long straight commuter track with no sharp bends, inclines or declines from Plesching to Mauthausen (B3 federal highway). Prior to the experiment, each participant was briefed, had to sign a consent form, and possible questions were answered by the experimenter. To reduce the effect of different traffic conditions, all test drives were scheduled on weekdays in the early afternoon (before start of the rush hour) or on weekends and at similar road and weather conditions. While on the road, data was continuously recorded (JSON file). After completion of the FOT, subjects were asked to fill in a NASA TLX to assess subjective workload differences between control and test groups. In addition, a questionnaire related to the usability of the app had to be filled. Subjects were finally
Figure 4: In-car setting for test group drivers with tablet computer installed near the center console.
group fuel speed [l] [km/h] BL 0.957 77.90 CG 0.973 78.82 TG 1.006 77.64
time [s] 948 961 975
Table 1: Average values for baseline (BL), control (CG), and test group (TG) while passing through the route.
debriefed and received no compensation for their effort. First, experienced drivers assigned to the baseline group used the app to collect optimal driving profiles that were later used to generate the recommendations. All the individual driving profiles differ only marginal, thus their aggregation should reflect how to drive optimally on the route. All the remaining subjects had no detailed knowledge about the route and were randomly assigned to either control or test group. Control group drivers completed the journey without any “App”-support while test group drivers received steering recommendations from the “Social driving app” (Figure 4). In this stage, the app provided – in addition to the ranking list and auditory feedback on the rank – only speed adaptation and gear changing recommendations. Quantitative and qualitative evaluation We are aware that, due to small sample size, differences in only one of the test drives (e. g., slow vehicle or truck ahead) could have caused a rather high impact on the overall results (this applies to all three groups baseline, control, test). Data collection was very well prepared, and recording scheduled at times with very similar traffic density. Nevertheless, it might be criticized that the quantitative results are not meaningful. To countervail, we concentrate below mainly on qualitative results. To verify H1, we first used a Fisher’s F-test to verify the homoskedasticity of series (Table 1). Results (F=0.227, p-value=0.37) leads us to the assumption that the two variances are homogeneous. According to a t-test (t(4)=0.434, p=0.34, α=0.05, single-tail), the null hypothesis is not significant, and H1 could not be accepted, i. e., we could not confirm the initial assumption that novice drivers or drivers not familiar with a route drive less efficient (in terms of fuel consumed) to drivers that use the route regularly. For H2, Fisher’s F-test leads us to accept the null hypothesis of homogeneity of variances for both cases (test, control group: F=3.786,
p-value=0.42), (test group, baseline: F=0.859, p-value=0.92). According to t-tests for homogeneous variances, the null hypothesis H20 is not significant at α=0.05 (single-tail), neither for test group vs. control group (t(4)=0.941, p=0.20, α=0.05) nor test group vs. baseline group (t(4)=1.072, p=0.17, α=0.05) and H2 could not be accepted. To conclude, for the current experiment we could not confirm that the test group has benefited from the steering recommendations provided by the “Social driving app”. The NASA TLX was used as a subjective assessment tool to rate the perceived workload of subjects while using the “Social driving app”. The evaluation of the test results in an average score of 10.55 (20-level Likert scale), which corresponds to medium demand and suggests that the subjects had no big issues in receiving recommendations from the app. No further peculiarities were found in the returned questionnaires. For the questions related to the usability of the app, user feedback was in general very positive. Users had to rate 15 questions on a 7-level Likert scale (1..don’t agree, 7..fully agree). Both the audio feedback on the ranking as well as the visual feedback (gear, speed) on the app were very well accepted by the users. Regarding audio feedback, participants were, in general, not annoyed by the auditory signals (avg. score=2.33). By receiving “applause” sound they felt confirmed with their driving behavior (score=6.0) while the “puuuh” sound encouraged them to drive “better” (score=5.66). No further issues indicated. According to evaluation results, users thought that the visual feedback (i. e., steering recommendations) was quite useful (avg. score=6.66) and they were not distracted from the primary task (score=2.0). Test participants also indicated that they did change their driving behavior due to the recommendations of the “Social driving app”. (This result is, however, not expressed in the driving data.) Asking about usability, most of the users indicated that they find such a system really useful (score=6.67) and that they
would like to have such a system in the future in their cars (score=6.33). For the question if participants of the test group think that they drove better than the control group without the driving recommendations, they responded with a clear “Yes” (score=6.0). Subjects had also no concerns about privacy, only one test participant mentioned that he would accept to use the system only if collected data is transmitted and stored anonymized. One reason for this outcome might be the fact that only young drivers aged 26 or under participated in the study. As a summary, based on the findings of this study, we believe that a social companion providing steering recommendations and implementing a ranking system to induce a behavior change in drivers, would be found useful and accepted by most of the drivers, and has potential to contribute to improved steering efficiency.
system. Test group drivers also thought that they actually drove better than the control group without activated recommendation system and they further mentioned that they would like to have such a system in the future in their own cars. These results motivate to further expedite research in the context of social driving. The functional demonstration in this work gives only a coarse impression about the real potential of such a recommendation service based on collective information sharing. The next steps are to run a larger test with more participants and/or a longitudinal test on a blocked road (e. g. race course) to generate more meaningful results. In addition, we are currently implementing a more generic version of the app that extracts driving information (gear, speed, braking/accelerating) only from sensors available on the Smartphone. Such an app would immediately allow to test the recommender system on a sort of macro scale.
Conclusion and outlook By merging all the information from drivers, cars, and infrastructure into a common database, the basis for an improved interaction between the involved parties could be established. In this work, we introduced the “Social driving app” aimed at improving driving efficiency (e. g., fuel economy, transit time, driving safety) by providing lay drivers or drivers unfamiliar with a certain route, recommendations about how to drive ideally on that track. Data like driving speed, gear to engage, etc. are collected from expert drivers with high yearly mileage or ample experience on that route, forwarded to a web service, processed, and recommendations are finally generated via the app for the less competent drivers. By introducing a ranking system to motivate the individual drivers to follow the instructions from the system, and, thus, to drive “better”, we further examined the influence of social aspects in car collectives. The small-scale field study could not confirm our hypothesis that the app has the potential to significantly reduce fuel consumption, but questionnaire results revealed that drivers liked the recommendation
References [1] Barkenbus, J. N. Eco-driving: An overlooked climate change initiative. Energy Policy 38, 2 (2010), 762–769. [2] den Brink, R. M. V., and Wee, B. V. Why has car-fleet specific fuel consumption not shown any decrease since 1990? quantitative analysis of dutch passenger car-fleet specific fuel consumption. Transportation Research Part D: Transport and Environment 6, 2 (2001), 75–93. [3] Department for Transport. The Fuel Efficient Truck Drivers’ Handbook. Tech. rep., Department for Transport,“FreightBestPractice”, Great Britain, April 2009. pp. 38. [4] Halgamuge, S., Manzie, C., and Watson, H. Fuel economy improvements for urban driving: Hybrid vs. intelligent vehicles. Transportation Research Part C: Emerging Technologies 15 (2007). [5] Manzie, C., Watson, H., and Halgamuge, S. Fuel economy improvements for urban driving: Hybrid vs. intelligent vehicles. Transportation Research Part C: Emerging Technologies 15, 1 (2007), 1–16. [6] Riener, A. Subliminal Persuasion and Its Potential for Driver Behavior Adaptation. In Transactions on Intelligent Transportation Systems, M. S. Vazquez, Ed., vol. 13, IEEE Computer Society (March 2012), 71–80. [7] Riener, A., and Ferscha, A. Enhancing Future Mass ICT with Social Capabilities. In Co-evolution of Intelligent Socio-technical Systems, E. Mitleton-Kelly, Ed. Springer, May 2013, 141–184. [8] Saboohi, Y., and Farzaneh, H. Model for developing an eco-driving strategy of a passenger vehicle based on the least fuel consumption. Applied Energy 86, 10 (2009), 1925–1932. [9] Tulusan, J., Staake, T., and Fleisch, E. Providing eco-driving feedback to corporate car drivers: What impact does a smartphone application have on their fuel efficiency? In Proceedings of the Conference on Ubiquitous Computing, UbiComp ’12, ACM (New York, NY, USA, 2012), 212–215.
