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In this first paper in a series we present 1298 low-redshift (z 0.2) optical spectra of 582 Type Ia supernovae (SNe Ia) observed from 1989 through 2008 as part of the Berkeley SN Ia Program (BSNIP). 584 spectra of 199 SNe Ia have well-calibrated light curves with measured distance moduli, and many of the spectra have been corrected for host-galaxy contamination. Most of the data were obtained using the Kast double spectrograph mounted on the Shane 3 m telescope at Lick Observatory and have a typical wavelength range of 3300-10,400Å, roughly twice as wide as spectra from most previously published datasets. We present our observing and reduction procedures, and we describe the resulting SN Database (SNDB), which will be an online, public, searchable database containing all of our fully reduced spectra and companion photometry. In addition, we discuss our spectral classification scheme (using the SuperNova IDentification code, SNID; Blondin & Tonry 2007), utilising our newly constructed set of SNID spectral templates. These templates allow us to accurately classify our entire dataset, and by doing so we are able to reclassify a handful of objects as bona fide SNe Ia and a few other objects as members of some of the peculiar SN Ia subtypes. In fact, our dataset includes spectra of nearly 90 spectroscopically peculiar SNe Ia. We also present spectroscopic host-galaxy redshifts of some SNe Ia where these values were previously unknown. The sheer size of the BSNIP dataset and the consistency of our observation and reduction methods makes this sample unique among all other published SN Ia datasets and is complementary in many ways to the large, low-redshift SN Ia spectra presented by Matheson et al. 2008 andBlondin et al. 2012. In other BSNIP papers in this series, we use these data to examine the relationships between spectroscopic characteristics and various observables such as photometric and host-galaxy properties.

In this second paper in a series, we present measurements of spectral features of 432 low‐redshift (z < 0.1) optical spectra of 261 Type Ia supernovae (SNe Ia) within 20 d of maximum brightness. The data were obtained from 1989 to the end of 2008 as part of the Berkeley Supernova Ia Program (BSNIP) and are presented in BSNIP I by Silverman et al. We describe in detail our method of automated, robust spectral feature definition and measurement which expands upon similar previous studies. Using this procedure, we attempt to measure expansion velocities, pseudo‐equivalent widths (pEWs), spectral feature depths and fluxes at the centre and endpoints of each of nine major spectral feature complexes. We investigate how velocity and pEW evolve with time and how they correlate with each other. Various spectral classification schemes are employed and quantitative spectral differences among the subclasses are investigated. Several ratios of pEW values are calculated and studied. The so‐called Si ii ratio, often used as a luminosity indicator, is found to be well correlated with the so‐called SiFe ratio and anticorrelated with the analogous ‘SSi ratio’, confirming the results of previous studies. Furthermore, SNe Ia that show strong evidence for interaction with circumstellar material or an aspherical explosion are found to have the largest near‐maximum expansion velocities and pEWs, possibly linking extreme values of spectral observables with specific progenitor or explosion scenarios. We find that purely spectroscopic classification schemes are useful in identifying the most peculiar SNe Ia. However, in almost all spectral parameters investigated, the full sample of objects spans a nearly continuous range of values. Comparisons to previously published theoretical models of SNe Ia are made and we conclude with a brief discussion of how the measurements performed herein and the possible correlations presented will be important for future SN surveys.

We present BVRI and unfiltered light curves of 93 Type Ia supernovae (SNe Ia) from the Lick Observatory Supernova Search (LOSS) follow-up program conducted between 2005 and 2018. Our sample consists of 78 spectroscopically normal SNe Ia, with the remainder divided between distinct subclasses (3 SN 1991bg-like, 3 SN 1991T-like, 4 SNe Iax, 2 peculiar, and 3 super-Chandrasekhar events), and has a median redshift of 0.0192. The SNe in our sample have a median coverage of 16 photometric epochs at a cadence of 5.4 d, and the median first observed epoch is ∼4.6 d before maximum B-band light. We describe how the SNe in our sample are discovered, observed, and processed, and we compare the results from our newly developed automated photometry pipeline to those from the previous processing pipeline used by LOSS. After investigating potential biases, we derive a final systematic uncertainty of 0.03 mag in BVRI for our data set. We perform an analysis of our light curves with particular focus on using template fitting to measure the parameters that are useful in standardizing SNe Ia as distance indicators. All of the data are available to the community, and we encourage future studies to incorporate our light curves in their analyses.

Driving requires forecasts. Forecasted movements of objects in the driving scene are uncertain. Inevitably, decision and control algorithms for autonomous driving need to cope with such uncertain forecasts. In assisted driving, the uncertainty in the human/vehicle interaction further increases the complexity of the control design task.Our research over the past ten years has focused on control design methods which systematically handle uncertain forecasts for autonomous and semi-autonomous vehicles. This article presents an overview of our findings and discusses relevant aspects of our recent results.