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CarMA: Towards personalized automotive tuning
Abstract and Figures
Wireless sensing and actuation have been explored in many contexts, but the automotive setting has received relatively little attention. Automobiles have tens of onboard sensors and expose several hundred engine parameters which can be tuned (a form of actuation). The optimal tuning for a vehicle can depend upon terrain, traffic, and road conditions, but the ability to tune a vehicle has only been available to mechanics and enthusiasts. In this paper, we describe the design and implementation of CarMA (Car Mobile Assistant), a system that provides high-level abstractions for sensing automobile parameters and tuning them. Using these abstractions, developers can easily write smart-phone "apps" to achieve fuel efficiency, responsiveness, or safety goals. Users of CarMA can tune their vehicles at the granularity of individual trips, a capability we call personalized tuning. We demonstrate through a variety of applications written on top of CarMA that personalized tuning can result in over 10% gains in fuel efficiency. We achieve this through route-specific or driver-specific customizations. Furthermore, CarMA is capable of improving user satisfaction by increasing responsiveness when necessary, and promoting vehicular safety by appropriately limiting the range of performance available to novice or unsafe drivers.
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... A second, less well-known, approach is to use the sensors embedded in a car [5], [6]. Some modern cars have several hundred physical and virtual (i.e., derived from physical) sensors onboard, which describe, in near-real time, the operation of several of the internal subsystems of the car. ...
... Access to such sensors is only becoming gradually available to external applications through special vehicle manufacturer developer programs. We have used an extended version of the CarMA software [5], [6] to collect traces of these sensor readings for our evaluations, from several different vehicles. ...
In-vehicle context sensing can detect many aspects of driver behavior and the environment, such as drivers changing lanes, stop signs, road grade and potholes, and these features can be used to improve driver safety and comfort, and engine efficiency. In general, detecting these features can use either on-board sensors on the vehicle (car sensors), or sensors built into mobile devices (phone sensors) carried by one or more occupants, or both. Furthermore, traces of sensor read- ings from different cars, when crowd-sourced, can provide increased spatial coverage as well as disambiguation. In this paper, we explore, by designing novel detection algorithms for the three different features discussed above, three related questions: How is the accuracy of detection related to the choice of phone vs. car sensors? To what extent, and in what ways, does crowd-sourcing contribute to detec- tion accuracy? How is accuracy affected by phone position? We have collected hundreds of miles of vehicle traces with annotated groundtruth, and demonstrated through evaluation that our detection algorithms can achieve high accuracy for each task (e.g. > 90% for lane change determinations) and that crowd-sensing plays an indispensable role in improving the detection performance (e.g. improving recall by 35% for lane change determinations on curves). Our results can give car manufacturers insight into how to augment their internal sensing capabilities with phone sensors, or give mobile app developers insight into what car sensors to use in order to complement mobile device sensing capabilities.
... To enable automotive context sensing, researchers have explored different methods to synthesize various automotive sensors to detect relevant driving events or road events. Procedural abstraction for programming vehicles is explored in [7] to tune and optimize the performance of a vehicle. Declarative programming [8] is used to express the behaviors of mobile events in the context of wireless sensor networks [9]. ...
Research in vehicular analytics has explored two different approaches to infer properties about a vehicle's surroundings: (a) using sensors on smartphone devices to infer properties about surroundings, or (b) to use in-vehicle sensors. The latter approach was less studied. In this paper, we take a first step beyond research to understand, using a pilot study, how to design vehicular analytics at scale. Our pilot prototype NORA (Network Oriented Road Applications) contains novel algorithms to detect roadside phenomena (such as potholes, rough road, and slippery surfaces). These leverage multiple in-vehicle sensors to disambiguate these phenomena from other conflating factors. NORA reliably detects, on a cloud service, roadside events from multiple individual in-vehicle detections. To do this, it uses careful clustering techniques to assess the spatial scale of the event, and belief decay techniques to match event duration. It also employs aggressive fleetwide suppression of detections to minimize communication cost. Through a 50-vehicle deployment of NORA pilot over 9 months, it is shown that the results obtained from NORA in-vehicle detection methods match very well with ground truth measurements, and NORA cloud is effective at aggregating road events in an accurate and efficient fashion.
... The elegant method simply partitions the 64-bit message frames into appropriately sized segments, and additionally considers tokens with reversed bit order (most significant bit is transmitted last rather than first). Similar to our approach, previous works have focused on leveraging UDS data as ground truth for analysis, e.g., [15][16][17], although they did not address the explicit problem of CAN data interpretation. Li et al. [17] presented an IDS that used a regression model to learn relationships between physical values, such as vehicle speed, and raw CAN data, whereas Wasicek et al. [16] develop an IDS based solely on anomalies in diagnostic data correlations. ...
... The elegant method simply partitions the 64-bit message frames into appropriately sized segments, and additionally considers tokens with reversed bit order (most significant bit is transmitted last rather than first). Similar to our approach, previous works have focused on leveraging UDS data as ground truth for analysis, e.g., [15][16][17], although they did not address the explicit problem of CAN data interpretation. Li et al. [17] presented an IDS that used a regression model to learn relationships between physical values, such as vehicle speed, and raw CAN data, whereas Wasicek et al. [16] develop an IDS based solely on anomalies in diagnostic data correlations. ...
Modern vehicles contain scores of Electrical Control Units (ECUs) that broadcast messages over a Controller Area Network (CAN). Vehicle manufacturers rely on security through obscurity by concealing their unique mapping of CAN messages to vehicle functions which differs for each make, model, year, and even trim. This poses a major obstacle for after-market modifications notably performance tuning and in-vehicle network security measures. We present ACTT: Automotive CAN Tokenization and Translation, a novel, vehicle-agnostic, algorithm that leverages available diagnostic information to parse CAN data into meaningful messages, simultaneously cutting binary messages into tokens, and learning the translation to map these contiguous bits to the value of the vehicle function communicated.
... A white paper from Redbend [1] summarizes the benefits of using FOTA updates in the automotive domain and shows the benefits of this technology for both OEMs and end users. In [13] the basic idea and the benefits of a dynamic SW update system are demonstrated. The system provides highlevel abstractions for sensing and tuning automobile parameters. ...
... The concept of Eco-driving has gained huge importance in recent years, intelligent driving techniques allow saving fuel, independent of the vehicle's embedded technology. Studies like Boriboonsomsin [6], Mierlo [7] and Flach [8] briefs the suitability of eco-driving assistants in making the users adopt an efficient driving style. A more efficient driving style would require an eco-driving assistant to evaluate the factors that lead to increased fuel consumption and generate feedback along with a driving advice targeted to a particular driving style that may not comply with fuel friendly driving styles. ...
... Similar methods for update of control software or for the code in any other safety critical ECU have yet to be developed and tested. Internet Connectivity for a vehicle has been used for personalized tuning of the vehicle in CarMA [15]. ...
In 2010, over 20.3 million vehicles were recalled. Software issues related to automotive controls such as cruise control, anti-lock braking system, traction control and stability control, account for an increasingly large percentage of the overall vehicles recalled. There is a need for new and scalable methods to evaluate automotive controls in a realistic and open setting. We have developed AutoPlug, an automotive Electronic Controller Unit (ECU) architecture between the vehicle and a Remote Diagnostics Center to diagnose, test, update and verify controls software. Within the vehicle, we evaluate observerbased runtime diagnostic schemes and introduce a framework for remote management of vehicle recalls. The diagnostics scheme deals with both real-time and non-real time faults, and we introduce a decision function to detect and isolate faults in a system with modeling uncertainties. We also evaluate the applicability of “Opportunistic Diagnostics”, where the observerbased diagnostics are scheduled in the ECU’s RTOS only when there is slack available in the system. This aperiodic diagnostics scheme performs similar to the standard, periodic diagnostics scheme under reasonable assumptions. This approach works on existing ECUs and does not interfere with current task sets. The overall framework integrates in-vehicle and remote diagnostics and serves to make vehicle recalls management a less reactive and cost-intensive procedure.
With the ongoing expansion of the aging population, it is increasingly critical to prioritize the safety of older drivers. The objective of this study is to utilize sensor data in order to detect early indications of impairment, thereby facilitating proactive interventions and enhancing road safety for the elderly. This article provides an overview of the research approach, presents significant results, and analyzes the consequences of utilizing in-vehicle sensors i.e. vision and telematics, to mitigate cognitive decline among elderly drivers; in doing so, it promotes progress in the domains of public health and transportation safety by standardizing the use of such devices to automatically assess the drivers’ cognitive functions.
Stress is a major concern in daily life, as it imposes significant and growing health and economic costs on society every year. Stress and driving are a dangerous combination and can lead to life-threatening situations, evidenced by the large number of road traffic crashes that occur every year due to driver stress. In addition, the rate of general health issues caused by work-related chronic stress in drivers who work in public and private transport is greater than in many other occupational groups. An in-vehicle warning system for driver stress levels is needed to
continuously predict dangerous driving situations and proactively alert drivers to ensure safe and comfortable driving. As a result of the recent developments in ambient intelligence, such as sensing technologies, pervasive devices, context recognition, and communications, driver stress can be automatically detected using multimodal measurements. This critical review investigates the state of the art of techniques and achievements for automatic driver stress level detection based on multimodal sensors and data. In this work, the most widely used data followed by frequent and highly performed selected features to detect driver stress levels are analyzed and presented. This review also discusses key methodological issues and gaps that hinder the implementation of driver stress detection systems and offers insights into future research directions.
Computer science paradigms such as internet of things and ubiquitous computing has led to the increase of data and information available for use in innovative projects. Smart cities are planned to harness the strengths of these technologies towards the benefit of society. In the case of urban transport, there are new opportunities for information dissemination and driving and traffic flow analysis. Smart devices, are an adequate choice to ubiquitously gather and transmit data unobtrusively. This fuels the opportunity to handle these data as an input for data analysis and fusion processes that discover and aggregate new information to notify users and communities of incorrect practices, thus aiming to effect behavioural change and intelligent planning. The PHESS driving platform is presented as a response to these requirements and as a realization of some of the potentials for ubiquitous computing in smart cities. Although, there are alternatives, this approach focuses on individual and community driving analysis, which differentiate it from other approaches.
Advanced internal combustion engine technologies have increased the number of accessible variables of an engine and our ability to control them. The optimal values of these variables are designated during engine calibration by means of a static correlation between the controllable variables and the corresponding steady-state engine operating points. While the engine is running, these correlations are being interpolated to provide values of the controllable variables for each operating point. These values are controlled by the electronic control unit to achieve desirable engine performance, for example in fuel economy, pollutant emissions, and engine acceleration. The state-of-the-art engine calibration cannot guarantee continuously optimal engine operation for the entire operating domain, especially in transient cases encountered in driving styles of different drivers. This paper presents the theoretical basis and algorithmic implementation for allowing the engine to learn the optimal set values of accessible variables in real time while running a vehicle. Through this new approach, the engine progressively perceives the driver's driving style and eventually learns to operate in a manner that optimizes specified performance indices. The effectiveness of the approach is demonstrated through simulation of a spark ignition engine, which learns to optimize fuel economy with respect to spark ignition timing, while it is running a vehicle.
Office buildings contain large sensor network de-ployments to monitor and maintain their internal envi-ronment. They also consume a significant amount of en-ergy. This paper proposes the use of the use of horizon-tal layering, rather than the current vertical-solution ap-proach, to expose the building data plane and enable in-teroporable software services and applications that mon-itor and control the building environment. We present our instantiation of this approach, which includes a data plane (sMAP) and storage service (IS4). Furthermore, we describe a set of applications built in this ecosystem.
In the drive to create the wired automobile, General Motors (GM) developed the OnStar system, a subscription-based, wireless, dashboard communication service that provides numerous safety and convenience features, from emergency assistance to remote door unlocking to hotel reservations. This case explores several key questions that GM faced regarding its market-leading telematics system. Should it syndicate OnStar, or was it of more value to the company as an exclusive feature? Should the OnStar division be spun off? Will telematics devices become standard for all vehicles, and if so, how would this affect decision making regarding OnStar?
In 2010, over 20.3 million vehicles were recalled. Software issues related to automotive controls such as cruise control, anti-lock braking system, traction control and stability control, account for an increasingly large percentage of the overall vehicles recalled. There is a need for new and scalable methods to evaluate automotive controls in a realistic and open setting. We have developed AutoPlug, an automotive Electronic Controller Unit (ECU) test-bed to diagnose, test, update and verify controls software. AutoPlug consists of multiple ECUs interconnected by a CAN bus, a race car driving simulator which behaves as the plant model and a vehicle controls monitor in Matlab. As the ECUs drive the simulated vehicle, the physics-based simulation provides feedback to the controllers in terms of acceleration, yaw, friction and vehicle stability. This closed-loop platform is then used to evaluate multiple vehicle control software modules such as traction, stability and cruise control. With this test-bed we highlight approaches for runtime ECU software diagnosis and testing of the stability and performance of the vehicle. Code updates can be executed via a smart phone so drivers may remotely “patch” their vehicle. This closed-loop automotive control test-bed allows the automotive research community to explore the capabilities and challenges of safe and secure remote code updates for vehicle recalls management.
The Transportation Energy Data Book: Edition 30 is a statistical compendium prepared and published by Oak Ridge National Laboratory (ORNL) under contract with the U.S. Department of Energy, Office of Energy Efficiency and Renewable Energy, Vehicle Technologies Program. Designed for use as a desk-top reference, the Data Book represents an assembly and display of statistics and information that characterize transportation activity, and presents data on other factors that influence transportation energy use. The purpose of this document is to present relevant statistical data in the form of tables and graphs. The latest edition of the Data Book is available to a larger audience via the Internet (cta.ornl.gov/data). This edition of the Data Book has 12 chapters which focus on various aspects of the transportation industry. Chapter 1 focuses on petroleum; Chapter 2 energy; Chapter 3 highway vehicles; Chapter 4 light vehicles; Chapter 5 heavy vehicles; Chapter 6 alternative fuel vehicles; Chapter 7 fleet vehicles; Chapter 8 household vehicles; Chapter 9 nonhighway modes; Chapter 10 transportation and the economy; Chapter 11 greenhouse gas emissions; and Chapter 12 criteria pollutant emissions. The sources used represent the latest available data. There are also three appendices which include detailed source information for some tables, measures of conversion, and the definition of Census divisions and regions. A glossary of terms and a title index are also included for the reader s convenience.
In this paper we develop a feedback algorithm for driving a highway vehicle for minimum fuel consumption. The algorithm allows the driver to choose an arbitrary steady state velocity for a level road, but modifies the speed for optimal fuel consumption on various grades. The algorithm is derived from the Pontryagin maximum principle to provide a mathematically optimal performance subject to the driver's choice of a trip time limit. Computer simulation is used to illustrate the implications of this analysis for gear shifts, braking and throttle settings as the road grade changes.
The Transportation Energy Data Book: Edition 11 is a statistical
compendium prepared and published by Oak Ridge National Laboratory
(ORNL) under contract with the Office of Transportation Technologies in
the Department of Energy (DOE). Designed for use as a desk-top
reference, the data book represents an assembly and display of
statistics and information that characterize transportation activity,
and presents data on other factors that influence transportation energy
use. The purpose of this document is to present relevant statistical
data in the form of tables and graphs. Each of the major transportation
modes - highway, air, water, rail, pipeline - is treated in separate
chapters or sections. Chapter 1 compares U.S. transportation data with
data from seven other countries. Aggregate energy use and energy supply
data for all modes are presented in Chapter 2. The highway mode, which
accounts for over three-fourths of total transportation energy
consumption, is dealt with in Chapter 3. Topics in this chapter include
automobiles, trucks, buses, fleet automobiles, Federal standards, fuel
economies, and household data. Chapter 4 is a new addition to the data
book series, containing information on alternative fuels and
alternatively-fueled vehicles. The last chapter, Chapter 5, covers each
of the nonhighway modes: air, water, pipeline, and rail, respectively.
Additional consumption of fuel in an intense traffic condition is inevitable. Excess fuel consumption may be avoided, if an optimal driving strategy is implemented subject to the surrounding condition of a vehicle and existing constraints. Development of an optimal driving strategy has been the subject of eco-driving. A model of optimal driving strategy has been developed and it has been applied for assessment of eco-driving rules. The model may be categorized as an optimal control and the objective function is minimization of fuel consumption in a given route. Vehicle speed and gear ratio are identified as control variables. The effect of working load has been considered according three engine running processes of Idle, part-load and wide open throttle. The model has then been applied to identify the optimal driving strategy of a vehicle in different traffic congestion based on eco-driving rules.