In a mandatory lane-changing scenario, vehicles need to move to their target lanes before the end of a road segment. Mandatory lane changing usually happens near a highway ramp, and it is one major source of traffic congestion and delay. The advance of connected and autonomous vehicles supports to solve the problem in a centralized and precise way. We formulate a mandatory lane-changing problem and propose a Mixed-Integer Linear Programming (MILP) approach to find an optimal solution. We then propose an MILP-based incremental-window algorithm to solve the problem efficiently. Experimental results show that the MILP-based incremental-window algorithm can take much less computation time and achieve almost the same solution quality, compared with the original MILP approach. The advantage of efficiency yet sufficient effectiveness matches the need of high-level control (decision of passing order) well.

A multi-mode software system contains several distinct modes of operation and a controller for deciding when to switch between modes. Even when developers rigorously test a multi-mode system before deployment, they cannot foresee and test for every possible usage scenario. As a result, unexpected situations in which the program fails or underperforms (for example, by choosing a non-optimal mode) may arise. This research aims to mitigate such problems by creating a new mode selector that examines the current situation, then chooses a mode that has been successful in the past, in situations like the current one. The technique, called program steering, creates a new mode selector via machine learning from good behavior in testing or in successful operation. Such a strategy, which generalizes the knowledge that a programmer has built into the system, may select an appropriate mode even when the original controller cannot. We have performed experiments on robot control programs written in a month-long programming competition. Augmenting these programs via our program steering technique had a substantial positive effect on their performance in new environments.

The paper proposes a fast noise estimation algorithm for speech enhancement using a perceptual Wiener filter. The noisy speech is decomposed using a critical-band-rate filterbank so that a perceptual modification of Wiener filtering can be applied in speech denoising. The subband noise estimate is updated by adaptively smoothing the noisy signal power. The smoothing parameter is chosen as a function of the estimated signal-to-noise ratio. This noise estimation technique gives accurate results even at very low signal-to-noise ratios, and works continuously, even in the presence of speech. It is effective for both non-stationary and coloured noise. Enhanced speech of good quality is obtained by the perceptual Wiener filter.

Considerable research attention has been directed towards speech and audio coding algorithms capable of producing high quality coded speech and audio, however few of these use signal representations which account for temporal as well as spectral detail. This paper presents a new technique for 16 kHz wideband speech and audio coding, whereby analysis and synthesis are performed using a linear phase gammatone filter bank. The outputs of these critical band filters are processed to obtain a series of pulse trains that represent neural firing. Auditory masking is then applied to reduce the number of pulses, producing a more compact time-frequency parameterization. The critical band gains and pulse amplitudes and positions are then coded using a combination of non-uniform quantization, arithmetic coding and vector quantization. This coding paradigm produces high quality coded speech and audio, is based upon well-known models of the auditory system, is highly scalable, and has moderate complexity.

We propose the novel application of a technique for filter design that can accurately fit measured tuning curves for the auditory fibres in the log-magnitude domain. This method provides pole-zero filters with guaranteed stability, and its log-magnitude domain criterion allows tuning curves with very steep slopes to be accurately modelled with an 8/sup th/ to 10/sup th/ order pole-zero filter. Thus, this technique can also be used to design a new set of critical band filters with superior frequency domain characteristics compared with the well-known gammatone filter bank. The filter bank designed using this technique has applications in auditory-based speech and audio analysis.

It is very difficult and costly to experimentally observe the motion of the basilar membrane in a fully functional cochlea with the view to obtaining amplitude response at points along the membrane. This paper presents an inexpensive method of generating psychoacoustic tuning curves from the well-known masking curves in critical band rate. We present a method for designing critical band auditory filters from the tuning curves. It is also known that the auditory filter frequency response becomes broader with increasing input signal levels and becomes narrower with decreasing signal levels. We also propose a method for designing level dependent auditory filters. The proposed filter bank is applicable to various types of signal processing required to model human auditory filtering.