Precise vehicle positioning is the fundamental technology in vehicle platooning, for sensing the driving environment, detecting hazards, and determining driving strategies. While radars offer robust performance in extreme weather, the positioning error of up to 1.8 m could be insufficient for platoons with vehicle spacings of a few meters. In contrast, LiDARs provide great accuracy, but its high cost hinders its penetration in the commercial market. To this end, this paper presents MatrixLoc, utilizing a projector as the headlight and a customized light sensor array as the receiver to achieve low-cost and centimeterlevel relative vehicle positioning in both longitudinal and lateral axes, along with bearing information. MatrixLoc leverages differential phase shift keying (DPSK) modulation to create a fringe pattern, enabling single-shot positioning. Accurate positioning is achieved by analyzing the observed space-varying frequency and demodulated phase information. Real-world driving results demonstrate that MatrixLoc achieves centimeter-level positioning accuracy, with a positioning error of 30 cm and a bearing error of 9 degrees at a distance of 20 m.

Cameras are widely adopted as the sensing device in fiducial marker systems. The captured videos however may expose sensitive information to attackers. We propose a privacy-preserving fiducial marker system, ReMark, which is based on retroreflector and single-pixel imaging (SPI), and captures only the minimal information needed for positioning and identifying markers. ReMark is built upon a state-of-the-art light-weight neural network (NN), which recovers the scene in the SPI system. Another light-weight NN is proposed for pose estimation of markers. They are trained with synthesized large datasets, and proved to adapt well to real-world data. The pose estimation NN adopts a specialized output embedding to address the symmetry-related issues, and soft-decision decoding is used to mitigate the distortion in recovered scenes. Detailed evaluation shows that, with 4.8 cm markers at 3.00 m distance and 1.0 W LED power, ReMark achieves a decode error rate of 2.1% with tilt angle ≤ 30°, searching in a dictionary of size 1,000. Evaluation of worst-case scenarios shows that an attacker could hardly acquire sensitive information even with access to raw data.

Connected autonomous vehicles have boosted a high demand on communication throughput in order to timely share the information collected by in-car sensors (e.g., LiDAR). While visible light communication (VLC) has shown its capability to offer Gigabit-level throughput for applications with high demand for data rate, most are performed indoors and the throughput of outdoor VLC drops to a few Mbps. To fill this performance gap, this paper presents RayTrack, an interference-free outdoor mobile VLC system. The key idea of RayTrack is to use a small but real-time adjustable FOV according to the transmitter location, which can effectively repel interference from the environment and from other transmitters and boost the system throughput. The idea also realizes virtual point-to-point links, and eliminates the need of link access control. To be able to minimize the transmitter detection time to only 20 ms, RayTrack leverages a high-compression-ratio compressive sensing scheme, incorporating a dual-photodiode architecture, optimized measurement matrix and Gaussian-based basis to increase sparsity. Real-world driving experiments show that RayTrack is able to achieve a data rate of 607.9 kbps with over 90% detection accuracy and lower than 15% bit error rate at 35 m, with 70 - 100 km/hr driving speed. To the best of our knowledge, this is the first working outdoor VLC system which can offer such range, throughput and error performance while accommodating freeway mobility.

Inertial measurement unit (IMU) has long suffered from the problem of integration drift, where sensor noises accumulate quickly and cause fast-growing tracking errors. Existing methods for calibrating IMU tracking either require human in the loop, or need energy-consuming cameras, or suffer from coarse tracking granularity. We propose to augment indoor inertial tracking by reusing existing indoor luminaries to project a static light polarization pattern in the space. This pattern is imperceptible to human eyes and yet through a polarizer, it becomes detectable by a color sensor, and thus can serve as fine-grained optical landmarks that constrain and correct IMU's integration drift and boost tracking accuracy. Exploiting the birefringence optical property of transparent tapes -- a low-cost and easily-accessible material -- we realize the polarization pattern by simply adding to existing light cover a thin polarizer film with transparent tape stripes glued atop. When fusing with IMU sensor signals, the light pattern enables robust, accurate and low-power motion tracking. Meanwhile, our approach entails low deployment overhead by reusing existing lighting infrastructure without needing an active modulation unit. We build a prototype of our light cover and the sensing unit using off-the-shelf components. Experiments show 4.3 cm median error for 2D tracking and 10 cm for 3D tracking, as well as its robustness in diverse settings.

We investigate the impact of realistic light radiation pattern on Vehicular VLC (V-VLC) based on an empirical study for typical car headlight and taillight modules. Our research community identified visible light as a communication technology that has a number of advantages to radio communication. Besides the huge spectrum that is fully unlicensed, the technology allows for very high data rates and reduced interference from parallel communications. After seeing a number of breakthroughs in indoor environments, the vehicular networking community got interested in the technology for a number of reasons as well. First successful applications have been reported but practical evidence is still limited. This is due to the use of very abstract simulation models and limited field testing as of now. We aim at bridging this gap by developing a realistic simulation model based on empirical data from a measurement campaign. Based on this model, we particularly investigate the impact of the light radiation pattern of typical car light modules on the communication performance. We can report that the angle and distance of the communicating cars have a significant impact and need to be carefully considered for developing V-VLC applications.

Accurate orientation information is the key in many applications, ranging from map reconstruction with crowdsourcing data, location data analytics, to accurate indoor localization. Many existing solutions rely on noisy magnetic and inertial sensor data, leading to limited accuracy, while others leverage multiple, dense anchor points to improve the accuracy, requiring significant deployment efforts. This paper presents LiCompass, the first system that enables a commodity camera to accurately estimate the object orientation using just a single optical anchor. Our key idea is to allow a camera to observe varying intensity level of polarized light when it is in different orientations and, hence, perform estimation directly from image pixel intensity. As the estimation relies only on pixel intensity, instead of the location of the anchor in an image, the system performs reliably at long distance, with low resolution images, and with large perspective distortion. LiCompass' core designs include an elaborate optical anchor design and a series of signal processing techniques based on trigonometric properties, which extend the range of orientation estimation to full 360 degrees. Our prototype evaluation shows that LiCompass produces very accurate estimates with median errors of merely 2.5 degrees at 5 meters and 7.4 degrees at 2.5 meters with an irradiance angle of 55 degrees.

Recent studies have demonstrated the potential of light-to-camera communications for realizing applications such as localization, augmented reality and vehicle-to-vehicle communications. However, the fundamental requirement of visible light communications, flicker-free illumination, becomes a key limitation hindering existing technologies from serving a camera at larger distances. To break this limitation, this paper presents POLI, a light-to-camera communication system that exploits a novel POlarized Light Intensity modulation scheme to provide reliable communications for a wide range of distances. The key idea of POLI is to hide the information with the polarization direction of the light, to which human eyes are insensitive. Taking advantage of this, POLI can change the intensity of the polarized light as slowly as possible, at a rate determined by the range the system would support, but does not generate flickers. By using an optical component to "transform polarization directions to colors", POLI allows a camera to leverage its received RGB values as the three spatial dimensions to recover the information carried in different data streams. POLI further incorporates a number of designs to tackle the non-linearity effect of a camera, which is especially critical for an intensity-based modulation scheme. We implemented a prototype using the USRP N200 combined with off-the-shelf optical components. The experimental results show that POLI delivers to its camera receiver a throughput proportional to the dynamic channel conditions. The achievable throughput can be up to 71 bytes per second at short distances, while the service range can be up to 40 meters.

Existing visible light positioning (VLP) systems leverage the high image resolution of a receiving camera to support high positioning accuracy. However, power-hungry cameras might not always be applicable for many scenarios, such as smart factories, in which small objects require to be accurately localized and tracked. In this paper, we introduce CELLI, an indoor VLP system that only uses a single luminary as the transmitter and requires only a simple light sensor to achieve an extremely high accuracy with centimeter-level error. The key idea is to provide the spatial resolution capability from the transmitter instead of the receiver, so that the complexity of the receiver can be minimized. In particular, a small LCD is installed at the transmitter to project a large number of narrow and interference-free polarized light beams to different spatial cells. A receiving light sensor identifies its located cell by detecting the unique polarization-modulated signals projected to that cell. CELLI further incorporates a number of novel designs to overcome the technical challenges such as reducing the positioning latency, which is typically limited by the long optical response time of an LCD, and transforming a cell coordinate to the global 3D position using only a single light. We have prototyped our design using off-the-shelf optical and electrical components, and experimentally shown that CELLI achieves a median 3D positioning error less than 11.8 cm and a median 2D positioning error to less than 2.7 cm.

Indoor positioning enables location-based services for a wide range of commercial applications [4]. Existing visible light positioning (VLP) systems [5, 7] leverage the high image resolution of a receiving camera to support high positioning accuracy. However, power-hungry cameras are not desirable in many scenarios, e.g., smart factories, where small objects need to be accurately located and tracked. In this demo, we introduce CELLI, an indoor VLP system that only uses a single luminary as the transmitter and requires only a simple light sensor to achieve high accuracy with centimeter-level error. The key idea is to provide the spatial resolution capability from the transmitter instead of the receiver, so that the complexity of the receiver can be minimized. In particular, a small Liquid Crystal Display (LCD) is installed at the transmitter to project a large number of narrow and interference-free polarized light beams to different spatial cells. A receiving light sensor identifies its located cell by detecting the unique polarization-modulated signals projected to that cell, as shown in Fig. 1. CELLI further incorporates several novel designs to overcome the technical challenges such as reducing the positioning latency, which is typically limited by the long optical response time of an LCD, and transforming a cell coordinate to the global 3D position using only a single light. We have prototyped our design using off-the-shelf optical and electronic components, and experimentally shown that CELLI achieves a median 3D positioning error less than 12 cm and a median 2D error less than 2.7 cm.

In this paper, we present a scooter collision avoidance system that can identify red-light runners (RLRs) at intersections. When the RLR behavior is detected, the system would advise the RLR to slow down immediately and warn nearby vehicles on the intersecting road in real time. In particular, we do not consider infrastructure-based solutions such as those utilizing a radar or a camera. This is because, in addition to high implementation costs, collisions can be only avoided at intersections where such infrastructure configurations are deployed. Instead, we advance an on-scooter solution using smartphones carried by scooter riders. Smartphones provide a useful platform that has a high penetration rate, more than sufficient computational power, inertial sensors to reflect the driving behavior, and the communication capability to transmit or receive information from other vehicles. In our system, we utilize a support vector machine and design an RLR classifier for learning and predicting RLR behaviors. The evaluation results show that our system is able to achieve over 70% recognition rates when distinguishing between RLR and non-RLR cases, as compared with approximately 80% recognition rates of the infrastructure-based (and higher cost) solution using a laser range finder (LADAR).

Recent literatures have demonstrated the feasibility and applicability of light-to-camera communications. They either use this new technology to realize specific applications, e.g., localization, by sending repetitive signal patterns, or consider non-line-of-sight scenarios. We however notice that line-of-sight light-to-camera communications has a great potential because it provides a natural way to enable visual association, i.e., visually associating the received information with the transmitter's identity. Such capability benefits broader applications, such as augmented reality, advertising, and driver assistance systems. Hence, this paper designs, implements, and evaluates RollingLight, a line-of-sight light-to-camera communication system that enables a light to talk to diverse off-the-shelf rolling shutter cameras. To boost the data rate and enhance reliability, RollingLight addresses the following practical challenges. First, its demodulation algorithm allows cameras with heterogeneous sampling rates to accurately decode high-order frequency modulation in real-time. Second, it incorporates a number of designs to resolve the issues caused by inherently unsynchronized light-to-camera channels. We have built a prototype of RollingLight with USRP-N200, and also implemented a real system with Arduino Mega 2560, both tested with a range of different camera receivers. We also implement a real iOS application to examine our real-time decoding capability. The experimental results show that, even to serve commodity cameras with a large variety of frame rates, RollingLight can still deliver a throughput of 11.32 bytes per second.

Camera communication (CamCom) is a form of visible light communication (VLC) that makes use of a camera as the receiver. One very important advantage of CamCom is that the acquired images during the receiving process provide a means to visually associate the physical object with the transmitted information, since the receiver has the knowledge of which pixels in the images are transmitting. Figure 1 shows a simple example. A user points her smartphone camera at multiple lights, touches the screen to select one, and uses the virtual switch that appears on the screen to adjust the lighting level. In this scenario, the lights simply periodically transmit simple identifiers, such as IP addresses, to the smartphone. The system is able to directly associate what the user sees (the image pixels corresponding to the light selected by the user) to the transmitted information (the identifier of the light). An out-of-band channel, such as WiFi, can then be used to send the actual command to the selected light to adjust the setting. Compared to the conventional approach of identifying a particular set of visual features unique to the object with computer vision techniques, this approach is simpler, more intuitive, and more accurate.

Visible light communication (VLC) has recently emerged to become a promising wireless communication technology. Vehicle lights and traffic lights have started to utilize LEDs and due to their shorter response time, they can be easily modified to become VLC transmitters. In addition, cameras embedded in smartphones can be used as VLC receivers. As a result, Vehicular VLC (V2LC) between vehicle lighting and smartphone cameras has the potential to enable a great number of applications with low cost. In this paper, a prototype V2LC system that utilizes undersampled frequency shift ON-OFF keying (UFSOOK) modulation is proposed. The system utilizes rolling shutter cameras as the receiver and takes advantages of its characteristics to improve the receiving performance. An off- the-shelf vehicle LED taillight is used as the transmitter. Information is transmitted in the continuous state (ON-OFF) changes of LEDs which are invisible to human eyes. The performance evaluation results demonstrate that the communication prototype is robust and can resist common optical interferences and noises within the image.

Abstract Visual obstruction caused by a preceding vehicle is one of the key factors threatening driving safety. One possible solution is to share the first‐person‐view of the preceding vehicle to unveil the blocked field‐of‐view of the following vehicle. However, the geometric inconsistency caused by the camera‐eye discrepancy renders view sharing between different cars a very challenging task. In this paper, we present a first‐person‐perspective image rendering algorithm to solve this problem. Firstly, we contour unobstructed view as the transferred region, then by iteratively estimating local homography transformations and performing perspective‐adaptive warping using the estimated transformations, we are able to locally adjust the shape of the unobstructed view so that its perspective and boundary could be matched to that of the occluded region. Thus, the composited view is seamless in both the perceived perspective and photometric appearance, creating an impression as if the preceding vehicle is transparent. Our system improves the driver's visibility and thus relieves the burden on the driver, which in turn increases comfort. We demonstrate the usability and stability of our system by performing its evaluation with several challenging data sets collected from real‐world driving scenarios.

Scooters are commonly used in many countries due to their low sale price, better fuel economy, and the ability to easily navigate through heavy traffic congestions. Today in Taiwan, approximately 70% of the registered vehicles are scooters. However, due to the low cost nature of the scooters, many safety technologies developed for cars cannot be adopted by them; statistics show that accidents involving scooters contribute to more than 80% of fatalities in traffic accidents in Taiwan, resulting in more than 2,000 deaths annually. It is therefore crucial to develop a new low-cost safety system that can be used by scooters. The enabling concept in this new safety system is cooperation between vehicles: scooters and cars sharing their current status and their observation of the neighboring environment via Vehicle-to-Vehicle (V2V) communications. However, how to implement this cost efficiently remains an open issue. Visible Light Communications (VLC) uses modulated visible light sources, i.e., changing the intensity of the light sent by the source, to transmit digital information, and Light Emitting Diodes (LEDs) are usually used as the transmission source. As LEDs become commonly used in automotive lighting, VLC appears as an attractive and cost-effective solution to implement V2V communications [1,2]: no extra cost is needed for the main transmission component and the additional processing circuits have very low complexity.

In Taiwan, accidents involving scooters have contributed to more than 70% of the total number of injuries and fatalities in traffic accidents and the numbers have been increasing for the past 8 years; it is therefore crucial to derive cost-effective safety solutions which can be used in scooters. To this end, we propose a cost-effective collision avoidance system which utilizes non-line-of-sight (nLoS) communication links between vehicles on intersecting roads. IEEE 802.15.4 link measurements were carried out to investigate the path loss and the packet reception rates of those nLoS links in three different intersections. Analysis of the results suggests that nLoS links at intersections, while exhibiting high path loss, are still more than sufficient to support the required vehicle-to-vehicle (V2V) communications for the proposed system to avoid 99% of the possible collisions at intersections.

Due to the increasing oil prices, reducing the weight of future cars will be important. One way to do this is by replacing wired sensors and their cables with wireless sensors. This could reduce the weight of cars, thus improving fuel efficiency. The replacement is anticipated to reduce the weight of an average passenger vehicle by up to 50 kg. However, such a replacement raises security concerns due to the intrinsic vulnerability of wireless technologies. In addressing security problems for intra-car wireless sensor networks, existing methods, focusing on energy efficiency and providing a low security level, are unfortunately not applicable. Our research focuses on providing a real-time and a high security solution. In this paper, we make four main contributions. First, we propose a systematic methodology to select security algorithms for real-time wireless sensor networks. Second, we determine the most effective combination with respect to the execution time and the security level by using our methodology. Third, the reported results are shown to be directly applicable to off-the-shelf sensor products and real-time wireless sensor networks, since we evaluated the performance on a real sensor platform and the real-time requirements for a car are much more stringent than other real-time systems. Finally, we suggest a mathematical methodology to increase the security level which imposes a burden only to adversaries, not to sensors.

Due to an increasing number of sensors deployed in cars, recently there is a growing interest in implementing a wireless sensor network within a car. In this paper, we report the results of packet transmission experiments using ZigBee sensor nodes within a car under various scenarios. The results of the experiments suggest that both Received Signal Strength Indicator (RSSI) and Link Quality Indicator (LQI) can only be used as a threshold-based indicator to evaluate the link quality - indicating poor link quality when dropping below a certain threshold. Preliminary experimental results show that a detection algorithm developed by the authors based on RSSI/LQI/error patterns and an adaptive strategy might increase the goodput performance of the link while improving power consumption of the radio.

In this paper we investigate the potential application of RFID technology in future cars as a cheaper, more reliable, and secure wireless alternative to the current wired operation in the engine compartment and other parts of a car where several sensors are connected to adapters which, in turn, are connected to a serial data bus for carrying the measured sensor data to various embedded microprocessors. The preliminary measurement results show that the coherence bandwidths of various channels between a reader and several RFID tags placed into different locations in the car are sufficiently large for the purpose of in-car sensor network communications while the power losses of certain channels are quite excessive and a special distributed antenna system may be required to deal with the problem.