Becoming economic: a political phenomenology of car purchases

"The point of this dissertation is to revisit the most ambiguous and perhaps most controversial aspect of Karl Polanyi's embeddedness thesis, namely the implication that socially disembedded economic action (i.e. action guided by a purely calculative disposition, ontologically separate from considerations of sociality) is ""always embedded"" (Block, 2003: 294) nonetheless. I aim, that is, to trouble and interrogate what it means to say that economic action is either embedded or disembedded. Yet what follows is less a re-evaluation of these ideas than a 'reboot,' given that Polanyi is rarely mentioned herein- less still Mark Granovetter, embeddedness' more recent champion. I call instead upon an altogether different set of protagonists: Daniel Miller and Michel Callon, who in 2002 and -5 squared off in a fruitful debate on the nature of economy. The analysis here adopts their terminology - entanglement versus disentanglement - as well as Miller's ethnographic sensibility, specifically of car purchases. Via semi-structured interviews with car buyers (N=39), I have sought to ascertain the determinants of the car-buying calculus and in doing so, to lay bare the socio-technical dynamics of automobile transactions. Putatively disentangled decision-making and -taking is entangled, I argue, with market/power, a neo-Foucauldian neologism emphasizing ways by which the buyer's sense of inferiority acts a focal point of market experience and subjectivity. Becoming economic in the context of an automobile acquisition (or any other major life purchase for that matter) is hence less a matter of optimally formatting one's calculative competencies than of reasonably justifying one's inferiority; of learning, that is, the crucial injunction to stop calculating. Another way of putting it, the market asymmetry that counts most is not the one between the buyer and seller, but rather the buyer and herself.

A Collaborative Sensor Fusion Algorithm for Multi-Object Tracking Using a Gaussian Mixture Probability Hypothesis Density Filter

This paper presents a method for collaborative tracking of multiple vehicles that extends a Gaussian Mixture Probability Hypothesis Density (GM-PHD) filter with a collaborative fusion algorithm. Measurements are preprocessed in a detect-before-track fashion, and cars are tracked using a rectangular shape model. The proposed method successfully mitigates clutter and occlusion problems. In order to extend the field of view of individual vehicles and increase the estimation confidence in the areas where a target is observable by multiple vehicles, PHD intensities are exchanged between vehicles and fused in the Collaborative GM-PHD filter using a novel algorithm based on the Generalized Covariance Intersection. The method is extensively evaluated using a calibrated, high-fidelity simulator in scenarios where vehicles exhibit both straight and curved motion at different speeds

Cooperative Multiple Dynamic Object Tracking on Moving Vehicles Based on Sequential Monte Carlo Probability Hypothesis Density Filter

This paper proposes a generalized method for tracking of multiple objects from moving, cooperative vehicles -- bringing together an Unscented Kalman Filter for vehicle localization and extending a Sequential Monte Carlo Probability Hypothesis Density filter with a novel cooperative fusion algorithm for tracking. The latter ensures that the fusion of information from cooperating vehicles is not limited to a fully overlapping Field Of View (FOV), as usually assumed in popular distributed fusion literature, but also allows for a perceptual extension corresponding to the union of the vehicles' FOV. Our method hence allows for an overall extended perception range for all cooperative vehicles involved, while preserving same or improving the accuracy in the overlapping FOV. This method also successfully mitigates noisy sensor measurement and clutter, as well as localization inaccuracies of tracking vehicles using Global Navigation Satellite Systems (GNSS). Finally, we extensively evaluate our method using a high-fidelity simulator for vehicles of varying speed and trajectories

A System Implementation and Evaluation of a Cooperative Fusion and Tracking Algorithm based on a Gaussian Mixture PHD Filter

This paper focuses on a real system implementation, analysis, and evaluation of a cooperative sensor fusion algorithm based on a Gaussian Mixture Probability Hypothesis Density (GM-PHD) filter, using simulated and real vehicles endowed with automotive-grade sensors. We have extended our previously presented cooperative sensor fusion algorithm with a fusion weight optimization method and implemented it on a vehicle that we denote as the ego vehicle. The algorithm fuses information obtained from one or more vehicles located within a certain range (that we call cooperative), which are running a multi-object tracking PHD filter, and which are sharing their object estimates. The algorithm is evaluated on two Citroen C-ZERO prototype vehicles equipped with Mobileye cameras for object tracking and lidar sensors from which the ground truth positions of the tracked objects are extracted. Moreover, the algorithm is evaluated in simulation using simulated C-ZERO vehicles and simulated Mobileye cameras. The ground truth positions of tracked objects are in this case provided by the simulator. Multiple experimental runs are conducted in both simulated and real-world conditions in which a few legacy vehicles were tracked. Results show that the cooperative fusion algorithm allows for extending the sensing field of view, while keeping the tracking accuracy and errors similar to the case in which the vehicles act alone

An Overtaking Decision Algorithm for Networked Intelligent Vehicles Based on Cooperative Perception

This paper presents an overtaking decision algorithm for networked intelligent vehicles. The algorithm is based on a cooperative tracking and sensor fusion algorithm that we previously developed. The ego vehicle is equipped with lane keeping and lane changing capabilities, as well as a forward-looking lidar sensor. The lidar data are fed to the tracking module which detects other vehicles, such as the vehicle that is to be overtaken (leading) and the oncoming traffic. Based on the estimated distances to the leading and the oncoming vehicles and their speeds, a risk is calculated and a corresponding overtaking decision is made. We compare the performance of the overtaking algorithm between the case when the ego vehicle only relies on its lidar sensor, and the case in which it fuses object estimates received from the leading car which also has a forward-looking lidar. Systematic evaluations are performed in Webots, a calibrated high-fidelity simulator

Distributed Graph-Based Control of Convoys of Heterogeneous Vehicles using Curvilinear Road Coordinates

This paper investigates the problem of controlling a heterogeneous group of vehicles with the aim of forming multi-lane convoys. We use a distributed, graph-based control law, implemented in a longitudinal coordinate system parallel to the road. Each vehicle maintains a local graph with information from only nearby vehicles, in which the desired distances between vehicles are calculated dynamically. This allows for fast adaptation to the changes in the number of vehicles and their positions. We have also implemented a distributed mechanism that allows vehicles to change lane in a cooperative way within the convoy. Systematic experiments have been carried out in a high-fidelity simulator in order to show the performance of the proposed control law

YIELD AND QUALITY OF MÜLLER-THURGAU CLONE GM11 OF NIS GRAPE GROWING REGION

This paper presents the results of research variety Müller-Thurgau, clone 11 Gm, in order to determine quality indicators and the possibility of expansion in the vineyards of Southern Serbia. In terms of Nis grape growing region of the test are the most important agro technological and economic characteristics of the clone 11 Gm compared to the standard variety Müller-Thurgau. The test clone exhibited significant differences in yield and quality of grapes

Distributed Graph-based Convoy Control for Networked Intelligent Vehicles

This paper presents an approach for formation control of multi-lane vehicular convoys in highways. We extend a Laplacian graph-based, distributed control law such that networked intelligent vehicles can join or leave the formation dynamically without jeopardizing the ensemble’s stability. Additionally, we integrate two essential control behaviors for lane-keeping and obstacle avoidance into the controller. To increase the performance of the convoy controller in terms of formation maintenance and fuel economy, the parameters of the controller are optimized in realistic scenarios using Particle Swarm Optimization (PSO), a powerful metaheuristic optimization method well-suited for large parameter spaces. The performances of the optimized controllers are evaluated in high-fidelity multi-vehicle simulations outlining the efficiency and robustness of the proposed strategy