Donald Grimm

CARLOG: a platform for flexible and efficient automotive sensing

Automotive apps can improve efficiency, safety, comfort, and longevity of vehicular use. These apps achieve their goals by continuously monitoring sensors in a vehicle, and combining them with information from cloud databases in order to detect events that are used to trigger actions (e.g., alerting a driver, turning on fog lights, screening calls). However, modern vehicles have several hundred sensors that describe the low level dynamics of vehicular subsystems, these sensors can be combined in complex ways together with cloud information. Moreover, these sensor processing algorithms may incur significant costs in acquiring sensor and cloud information. In this paper, we propose a programming framework called CARLOG to simplify the task of programming these event detection algorithms. CARLOG uses Datalog to express sensor processing algorithms, but incorporates novel query optimization methods that can be used to minimize bandwidth usage, energy or latency, without sacrificing correctness of query execution. Experimental results on a prototype show that CARLOG can reduce latency by nearly two orders of magnitude relative to an unoptimized Datalog engine.

Towards Robust Vehicular Context Sensing

In-vehicle context sensing can detect many aspects of driver behavior and the environment, such as drivers changing lanes, potholes, road grade, and stop signs, and these features can be used to improve driver safety and comfort, and engine efficiency. In general, detecting these features can use either onboard 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 readings 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 four different features discussed above, three related questions: How is the accuracy of detection related to the choice of phone versus car sensors? To what extent, and in what ways, does crowd-sourcing contribute to detection 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.,

Poster: CARLOC: Precisely Tracking Automobile Position

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Audible warning for vehicle safety systems

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.

CARLOC: Precise Positioning of Automobiles

Precise positioning of an automobile to within lane-level precision can enable better navigation and context-awareness. However, GPS by itself cannot provide such precision in obstructed urban environments. In this paper, we present a system called CARLOC for lane-level positioning of automobiles. CARLOC uses three key ideas in concert to improve positioning accuracy: it uses digital maps to match the vehicle to known road segments; it uses vehicular sensors to obtain odometry and bearing information; and it uses crowd-sourced location of estimates of roadway landmarks that can be detected by sensors available in modern vehicles. CARLOC unifies these ideas in a probabilistic position estimation framework, widely used in robotics, called the sequential Monte Carlo method. Through extensive experiments on a real vehicle, we show that CARLOC achieves sub-meter positioning accuracy in an obstructed urban setting, an order-of-magnitude improvement over a high-end GPS device.