Cooperative Perception for Social Driving in Connected Vehicle Traffic

The development of autonomous vehicle technology has moved to the center of automotive research in recent decades. In the foreseeable future, road vehicles at all levels of automation and connectivity will be required to operate safely in a hybrid traffic where human operated vehicles (HOVs) and fully and semi-autonomous vehicles (AVs) coexist. Having an accurate and reliable perception of the road is an important requirement for achieving this objective. This dissertation addresses some of the associated challenges via developing a human-like social driver model and devising a decentralized cooperative perception framework. A human-like driver model can aid the development of AVs by building an understanding of interactions among human drivers and AVs in a hybrid traffic, therefore facilitating an efficient and safe integration. The presented social driver model categorizes and defines the driver\u27s psychological decision factors in mathematical representations (target force, object force, and lane force). A model predictive control (MPC) is then employed for the motion planning by evaluating the prevailing social forces and considering the kinematics of the controlled vehicle as well as other operating constraints to ensure a safe maneuver in a way that mimics the predictive nature of the human driver\u27s decision making process. A hierarchical model predictive control structure is also proposed, where an additional upper level controller aggregates the social forces over a longer prediction horizon upon the availability of an extended perception of the upcoming traffic via vehicular networking. Based on the prediction of the upper level controller, a sequence of reference lanes is passed to a lower level controller to track while avoiding local obstacles. This hierarchical scheme helps reduce unnecessary lane changes resulting in smoother maneuvers. The dynamic vehicular communication environment requires a robust framework that must consistently evaluate and exploit the set of communicated information for the purpose of improving the perception of a participating vehicle beyond the limitations. This dissertation presents a decentralized cooperative perception framework that considers uncertainties in traffic measurements and allows scalability (for various settings of traffic density, participation rate, etc.). The framework utilizes a Bhattacharyya distance filter (BDF) for data association and a fast covariance intersection fusion scheme (FCI) for the data fusion processes. The conservatism of the covariance intersection fusion scheme is investigated in comparison to the traditional Kalman filter (KF), and two different fusion architectures: sensor-to-sensor and sensor-to-system track fusion are evaluated. The performance of the overall proposed framework is demonstrated via Monte Carlo simulations with a set of empirical communications models and traffic microsimulations where each connected vehicle asynchronously broadcasts its local perception consisting of estimates of the motion states of self and neighboring vehicles along with the corresponding uncertainty measures of the estimates. The evaluated framework includes a vehicle-to-vehicle (V2V) communication model that considers intermittent communications as well as a model that takes into account dynamic changes in an individual vehicle’s sensors’ FoV in accordance with the prevailing traffic conditions. The results show the presence of optimality in participation rate, where increasing participation rate beyond a certain level adversely affects the delay in packet delivery and the computational complexity in data association and fusion processes increase without a significant improvement in the achieved accuracy via the cooperative perception. In a highly dense traffic environment, the vehicular network can often be congested leading to limited bandwidth availability at high participation rates of the connected vehicles in the cooperative perception scheme. To alleviate the bandwidth utilization issues, an information-value discriminating networking scheme is proposed, where each sender broadcasts selectively chosen perception data based on the novelty-value of information. The potential benefits of these approaches include, but are not limited to, the reduction of bandwidth bottle-necking and the minimization of the computational cost of data association and fusion post processing of the shared perception data at receiving nodes. It is argued that the proposed information-value discriminating communication scheme can alleviate these adverse effects without sacrificing the fidelity of the perception

Real Estate Income and Value Cycles: A Model of Market Dynamics

We develop a theoretical real estate cycles model linking economic fundamentals to real estate income and value. We estimate and test an econometric model specification, based on the theoretical model, using MSA level data for twenty office markets in the United States. Our major conclusion is that cities that exhibit seemingly different cyclical office market behavior may be statistically characterized by our three-parameter econometric specification. The parameters are MSA-specific amplitude, through the CAP rate, cycle duration (peak-to-peak), via the rate of partial adjustments to changing expectations about stabilized NOI and the market trend.

Restoration of axon conduction and motor deficits by therapeutic treatment with glatiramer acetate.

Glatiramer acetate (GA; Copaxone) is an approved drug for the treatment of multiple sclerosis (MS). The underlying multifactorial anti-inflammatory, neuroprotective effect of GA is in the induction of reactive T cells that release immunomodulatory cytokines and neurotrophic factors at the injury site. These GA-induced cytokines and growth factors may have a direct effect on axon function. Building on previous findings that suggest a neuroprotective effect of GA, we assessed the therapeutic effects of GA on brain and spinal cord pathology and functional correlates using the chronic experimental autoimmune encephalomyelitis (EAE) mouse model of MS. Therapeutic regimens were utilized based on promising prophylactic efficacy. More specifically, C57BL/6 mice were treated with 2 mg/mouse/day GA for 8 days beginning at various time points after EAE post-induction day 15, yielding a thorough, clinically relevant assessment of GA efficacy within the context of severe progressive disease. Therapeutic treatment with GA significantly decreased clinical scores and improved rotorod motor performance in EAE mice. These functional improvements were supported by an increase in myelinated axons and fewer amyloid precursor protein-positive axons in the spinal cords of GA-treated EAE mice. Furthermore, therapeutic GA decreased microglia/macrophage and T cell infiltrates and increased oligodendrocyte numbers in both the spinal cord and corpus callosum of EAE mice. Finally, GA improved callosal axon conduction and nodal protein organization in EAE. Our results demonstrate that therapeutic GA treatment has significant beneficial effects in a chronic mouse model of MS, in which its positive effects on both myelinated and non-myelinated axons results in improved axon function

On-Board Sensor-Based NO x Emissions from Heavy-Duty Diesel Vehicles

Real-world nitrogen oxides (NOx) emissions were estimated using on-board sensor readings from 72 heavy-duty diesel vehicles (HDDVs) equipped with a Selective Catalytic Reduction (SCR) system in California. The results showed that there were large differences between in-use and certification NOx emissions, with 12 HDDVs emitting more than three times the standard during hot-running and idling operations in the real world. The overall NOx conversion efficiencies of the SCR system on many vehicles were well below the 90% threshold that is expected for an efficient SCR system, even when the SCR system was above the optimum operating temperature threshold of 250 °C. This could potentially be associated with SCR catalyst deterioration on some engines. The Not-to-Exceed (NTE) requirements currently used by the heavy-duty in-use compliance program were evaluated using on-board NOx sensor data. Valid NTE events covered only 4.2–16.4% of the engine operation and 6.6–34.6% of the estimated NOx emissions. This work shows that low cost on-board NOx sensors are a convenient tool to monitor in-use NOx emissions in real-time, evaluate the SCR system performance, and identify vehicle operating modes with high NOx emissions. This information can inform certification and compliance programs to ensure low in-use NOx emissions

Proactive and reactive cognitive control and dorsolateral prefrontal cortex dysfunction in first episode schizophrenia.

Cognitive control deficits have been consistently documented in patients with schizophrenia. Recent work in cognitive neuroscience has hypothesized a distinction between two theoretically separable modes of cognitive control-reactive and proactive. However, it remains unclear the extent to which these processes are uniquely associated with dysfunctional neural recruitment in individuals with schizophrenia. This functional magnetic resonance imaging (fMRI) study utilized the color word Stroop task and AX Continuous Performance Task (AX-CPT) to tap reactive and proactive control processes, respectively, in a sample of 54 healthy controls and 43 patients with first episode schizophrenia. Healthy controls demonstrated robust dorsolateral prefrontal, anterior cingulate, and parietal cortex activity on both tasks. In contrast, patients with schizophrenia did not show any significant activation during proactive control, while showing activation similar to control subjects during reactive control. Critically, an interaction analysis showed that the degree to which prefrontal activity was reduced in patients versus controls depended on the type of control process engaged. Controls showed increased dorsolateral prefrontal cortex (DLPFC) and parietal activity in the proactive compared to the reactive control task, whereas patients with schizophrenia did not demonstrate this increase. Additionally, patients' DLPFC activity and performance during proactive control was associated with disorganization symptoms, while no reactive control measures showed this association. Proactive control processes and concomitant dysfunctional recruitment of DLPFC represent robust features of schizophrenia that are also directly associated with symptoms of disorganization

Upper extremity compartment syndrome after minor trauma: an imperative for increased vigilance for a rare, but limb-threatening complication

BACKGROUND: Compartment syndrome of any extremity is a limb-threatening emergency requiring an emergent surgical management. Thus, ruling out compartment syndrome is often high on the list of priorities when treating high-energy injuries and fractures. However, even in the most seemingly benign injuries, this dangerous diagnosis must always remain on the differential and suspicion must remain high. CASE PRESENTATION: 23-year-old factory worker presents after a low energy entrapment injury to his left forearm. Initial work-up and evaluation noted an isolated radial head dislocation with a normal physical motor and sensory exam. However, maintaining high suspicion for compartment syndrome despite serial normal physical exams, led objective compartment pressure measurement leading to definitive diagnosis. Emergent surgical intervention via compartment fasciotomies was performed, along with closed reduction and ligament repair. At 1 year follow-up, the patient was well-healed, back to work with full range of motion and not activity limitations. CONCLUSION: Despite a seemingly benign injury pattern, and a relatively low energy mechanism, vigilant concern for compartment syndrome following any kind of entrapment injury should initiate serial examinations and compartment pressure measurements especially in circumstances with continued swelling and inability to perform an accurate clinical assessment due to an obtunded or medicated patient

Lithium Salt Effects on Silicon Electrode Performance and Solid Electrolyte Interphase (SEI) Structure, Role of Solution Structure on SEI Formation

Silicon electrodes were cycled with electrolytes containing different salts to investigate the effect of salt on the electrochemical performance and SEI structure. Comparable capacity retention were observed for the 1.2 M LiPF6, LiTFSI and LiClO4 electrolytes in ethylene carbonate (EC):dimethyl carbonate (DEC), 1:1, but severe fading was observed for the 1.2 M LiBF4 electrolyte. The differential capacity plots and EIS analysis reveals that failure of the 1.2 M LiBF4 electrolyte is attributed to large surface resistance and increasing polarization upon cycling. However, when LiBF4 was added as an electrolyte additive (10% LiBF4 and 90% LiPF6), the capacity retention and Coulombic efficiency were improved. The SEI was analyzed by FTIR and XPS for each electrolyte. Both spectroscopic methods suggest that the main components of the SEI are lithium ethylene dicarbonate (LEDC) and Li2CO3 in the 1.2 M LiPF6, LiTFSI and LiClO4 electrolytes, while an inorganic-rich SEI, composed of LiF and borates, was generated for both the 1.2 M LiBF4 electrolyte and the 10% LiBF4 electrolyte. The chemical composition of the SEIs and corresponding electrochemical performance of the Si electrodes were strongly correlated with electrolyte solution structure

Relative entropy as an index of soil structure

Soil structure controls key soil functions in both natural and agro-ecosystems. Thus, numerous attempts have been made to develop methods aiming at its characterization. Here we propose an index of soil structure that uses relative entropy to quantify differences in the porosity and pore(void)-size distribution (VSD) between a structured soil derived from soil water retention data and the same soil without structure (a so-called reference soil) estimated from its particle-size distribution (PSD). The difference between these VSDs, which is the result of soil structure, is quantified using the Kullback-Leibler Divergence (KL divergence). We applied the method to soil data from two Swedish field experiments that investigate the long-term effects of soil management (fallow vs. inorganic fertilizer vs. manure) and land use (afforested land vs. agricultural land dominated by grass/clover ley) on soil properties. The KL divergence was larger for the soil receiving regular addition of manure compared with the soils receiving no organic amendments. Furthermore, soils under afforested land showed significantly larger KL divergences compared to agricultural soils near the soil surface, but smaller KL divergences in deeper soil layers, which closely mirrored the distribution of organic matter in the soil profile. Indeed, a significant positive correlation (r = 0.374, p < 0.001) was found between soil organic carbon concentrations and KL divergences across all sites and treatments. Despite challenges related to modelling the VSD of the reference soil without structure, the proposed index proved useful for evaluating differences in soil structure in response to soil management and land-use change and reflected the expected effects of soil organic matter on soil structure. We conclude that relative entropy shows great potential to serve as an easy-to-use index of soil structure, as it only requires widely available data on soil physical and hydraulic properties. Highlights A new index of soil structure is proposed based on relative entropy A method is developed that separates the effects of soil texture and structure on the pore space The index identified soil structural differences in response to land use and soil organic carbon concentrations (SOC) The index shows the potential to serve as an easy-to-use metric of soil structur