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Autism Spectrum Disorder (ASD) is a common neurodevelopmental disturbance afflicting a variety of functions. The recent computational focus suggesting aberrant Bayesian inference in ASD has yielded promising but conflicting results in attempting to explain a wide variety of phenotypes by canonical computations. Here, we used a naturalistic visual path integration task that combines continuous action with active sensing and allows tracking of subjects’ dynamic belief states. Both groups showed a previously documented bias pattern by overshooting the radial distance and angular eccentricity of targets. For both control and ASD groups, these errors were driven by misestimated velocity signals due to a nonuniform speed prior rather than imperfect integration. We tracked participants’ beliefs and found no difference in the speed prior, but there was heightened variability in the ASD group. Both end point variance and trajectory irregularities correlated with ASD symptom severity. With feedback, variance was reduced, and ASD performance approached that of controls. These findings highlight the need for both more naturalistic tasks and a broader computational perspective to understand the ASD phenotype and pathology.

Accuracy and efficiency for nanomanipulation inside a scanning electron microscope (SEM) remain a major concern for researchers. So far, various methods have been proposed based on visual servo control. Almost all of them require a proper tracking speed prior to driving the manipulators to keep end effectors and samples in the field of view. In this study, we proposed a speed adaptive method based on pixels under different magnifications to obtain tracking speed automatically. A nanorobotic manipulation system with 16 DOFs was designed to verify the proposed speed adaptive method. This system is configured under an SEM composed of four basic manipulator units. Feasibility of this method is shown by performing route tracking in the proposed G-code and point-driven mode. As a general-purposed manipulation process, the proposed speed adaptive method and the drive modes will enable a variety of applications in such fields such as nanofabrication, assembly, electrical and mechanical characterization of low dimensional functional materials.

Considering the speed information as a prior constraint, constrained cubature kalman filter (CCKF) tracking filtering method based on model reduction (MR) method and perfect measurement (PM) method is studied and compared for the tracking problem of fixed single observer passive positioning using only angle pair constant speed targets. The simulation results show that the fixed single observer passive positioning system can track the moving target with constant speed by measuring angle only under the condition of speed prior information, and the perfect measurement method is more robust to the initial state of the filter than the model reduction method.

Autism Spectrum Disorder (ASD) is a common neurodevelopmental disturbance afflicting a variety of functions from perception to cognition. The recent computational focus suggesting aberrant Bayesian inference in ASD has yielded promising but conflicting results in attempting to explain a wide variety of phenotypes by canonical computations. Here we used a naturalistic visual path integration task that combines continuous action with active sensing and allows tracking of subjects’ dynamic belief states. Both groups showed a previously documented bias pattern, by overshooting the radial distance and angular eccentricity of targets. For both control and ASD groups, these errors were driven by misestimated velocity signals due to a non-uniform speed prior, rather than imperfect integration. We tracked participant’s beliefs and found no difference in the speed prior, but heightened variability in the ASD group. Both end-point variance and trajectory irregularities correlated with ASD symptom severity. With feedback, variance was reduced and ASD performance approached that of controls. These findings highlight the need for both more naturalistic tasks and a broader computational perspective to understand the ASD phenotype and pathology.

We take a multidimensional perspective on the development of second language (L2) speaking ability and examine how changes in the underlying cognitive variables of linguistic knowledge and processing speed interact with complexity, fluency, and accuracy over the course of a 3-month Spanish study abroad session. Study abroad provides a unique learning context for evaluating changes in the under- lying dimensions of L2 speaking because learners are fully immersed in the target language and have ample opportunity to implement, practice, and integrate newly gained skills. Participants were 39 native English speakers acquiring Spanish in Argentina. Results show that participants experienced significant gains across complexity, fluency, and accuracy. However, these gains were not evenly distributed across all dimensions or across all learners. Learners with higher levels of L2 linguistic knowledge and faster L2 processing speed prior to study abroad experienced greater gains in accuracy and syntactic and lexical complexity during study abroad. [ABSTRACT FROM AUTHOR]

Connected Vehicles (CV) technology has been used to address safety issues on highway horizontal curves. Existing curve warning systems are either using curve warning signs or providing drivers with an in-vehicle curve warning message in advance, allowing drivers to adjust their speed prior to the vehicle entering the curve. In practice, drivers might be compliant before entering the curve but may pick up the speed in the curve. Therefore, it remains a problem that existing curve warning systems are not able to guide drivers by providing necessary speed warnings through the entire course of approaching, entering, navigating, and leaving horizontal curves. Therefore, the objective of this study is to improve curve speed compliance by proposing a guidance-oriented Advanced Curve Speed Warning system (Advanced-CSW) with a focus on providing guided curve speed messages throughout the horizontal curves. The Advanced-CSW system is based on Dedicated Short-Range Communication (DSRC) enabling vehicle-infrastructure (V2I) communication. Anytime the vehicle is speeding, the guided message will be displayed until the vehicle’s speed is within compliant range. Drivers who use the Advanced-CSW can receive multiple guided messages from the in-vehicle heads-up display through the entire course of navigating through horizontal curves. Thirty participants are recruited to perform the driving experiment on the simulator of driving through a series of horizontal curves under various geometric, roadway and traffic conditions. These conditions include different curve severity, illumination, and pavement wetness levels. The Advanced-CSW system’s performance was evaluated in terms of the speed difference, which measures the gap between the in-curve mean speed and curve advisory speed. The results were compared with the performance of speed difference by driving with CSW or CSO through the entire curve. The experiment data was modeled using the mixed linear model with random effects, which includes the individual’s driving behavior. In summary, when male drivers navigate through the horizontal curves under different curve speed warning systems, their speed compliance is significantly increased with continuous and guided messages provided in comparison with the speed compliance under the one-time curve speed warning message and the curve sign only. Female drivers improve their speed compliance in the curve by using curve signs only comparing to using one-time curve speed warning message or continuous guided curve speed warning messages. Also, male drivers’ speed differences by using the guided system are significantly reduced by 6.53∼7.68 mi/h compared to driving with curve signs only or one-time curve speed warning message. In addition, there is also a speed reduction of 1.81 mi/h if male drivers receiving continuous guided messages in the curve during the daytime than during the nighttime. The proposed adaptive system based on that is adaptive to the vehicle’s real-time speed and location by providing a new direction in designing effective curve warning systems. The speed-guided messages through the entire course of approaching, entering, navigating, and leaving horizontal curves can solve the current issue of speed incompliance by using the existing curve warning systems.

Object motion in natural scenes results in visual stimuli with a rich and broad spatiotemporal frequency spectrum. While the question of how the visual system detects and senses motion energies at different spatial and temporal frequencies has been fairly well studied, it is unclear how the visual system integrates this information to form coherent percepts of object motion. We applied a combination of tailored psychophysical experiments and predictive modeling to address this question with regard to perceived motion in a given direction (i.e., stimulus speed). We tested human subjects in a discrimination experiment using stimuli that selectively targeted four distinct spatiotemporally tuned channels with center frequencies consistent with a common speed. We first characterized subjects' responses to stimuli that targeted only individual channels. Based on these measurements, we then predicted subjects' psychometric functions for stimuli that targeted multiple channels simultaneously. Specifically, we compared predictions of three Bayesian observer models that either optimally integrated the information across all spatiotemporal channels, or only used information from the most reliable channel, or formed an average percept across channels. Only the model with optimal integration was successful in accounting for the data. Furthermore, the proposed channel model provides an intuitive explanation for the previously reported spatial frequency dependence of perceived speed of coherent object motion. Finally, our findings indicate that a prior expectation for slow speeds is added to the inference process only after the sensory information is combined and integrated.

Heavy-duty vehicle platooning has been an important research topic in recent years. By driving closely together, the vehicles save fuel by reducing total air drag and utilize the road more efciently Often the heavy-duty vehicles will catch-up in order to platoon while driving on the common stretch of road, and in this case, a good prediction of when the platoon merging will take place is required in order to make predictions on overall fuel savings and to automatically control the velocity prior to the merge. The vehicle speed prior to platoon merging is mostly infuenced by the road grade and by the local trafc condition. In this paper, we examine the infuence of road grade and propose a method for predicting platoon merge distance using vehicle speed prediction based on road grade. The proposed method is evaluated using experimental data from platoon merging test runs done on a highway with varying level of trafc. It is shown that under reasonable conditions, the error in the merge distance prediction is smaller than 8%.

The perceived speed of moving objects has long been known to depend on image contrast. Lowering the contrast of first-order motion stimuli typically decreases perceived speed – the well-known “Thompson effect”. It has been suggested that contrast-dependent biases are the result of optimal inference by the visual system, whereby unreliable sensory information is combined with prior beliefs. The Thompson effect is thought to result from the prior belief that objects move slowly (in Bayesian terminology, a “slow speed prior”). However, there is some evidence that the Thompson effect is attenuated or even reversed at higher speeds. Does the effect of contrast on perceived speed depend on absolute speed and what does this imply for Bayesian models with a slow speed prior? We asked subjects to compare the speeds of simultaneously presented drifting gratings of different contrasts. At low contrasts (3–15%), we found that the Thompson effect was attenuated at high speeds: at 8 and 12deg/s, perceived speed increased less with contrast than at 1 and 4deg/s; however, at higher contrasts (15–95%), the situation was reversed. A semi-parametric Bayesian model was used to extract the subjects’ speed priors and was subsequently improved by combining it with a model of speed tuning. These novel findings regarding the dual, contrast-dependent effect of high speeds help reconcile existing conflicting literature and suggest that physiologically plausible mechanisms of representation of speed in the visual cortex may need to be incorporated into Bayesian models to account for certain subtleties of human speed perception.

Severe crashes are causing serious social and economic loss, and because of this, reducing crash injury severity has become one of the key objectives of the high speed facilities' (freeway and expressway) management. Traditional crash injury severity analysis utilized data mainly from crash reports concerning the crash occurrence information, drivers' characteristics and roadway geometric related variables. In this study, real-time traffic and weather data were introduced to analyze the crash injury severity. The space mean speeds captured by the Automatic Vehicle Identification (AVI) system on the two roadways were used as explanatory variables in this study; and data from a mountainous freeway (I-70 in Colorado) and an urban expressway (State Road 408 in Orlando) have been used to identify the analysis result's consistence. Binary probit (BP) models were estimated to classify the non-severe (property damage only) crashes and severe (injury and fatality) crashes. Firstly, Bayesian BP models' results were compared to the results from Maximum Likelihood Estimation BP models and it was concluded that Bayesian inference was superior with more significant variables. Then different levels of hierarchical Bayesian BP models were developed with random effects accounting for the unobserved heterogeneity at segment level and crash individual level, respectively. Modeling results from both studied locations demonstrate that large variations of speed prior to the crash occurrence would increase the likelihood of severe crash occurrence. Moreover, with considering unobserved heterogeneity in the Bayesian BP models, the model goodness-of-fit has improved substantially. Finally, possible future applications of the model results and the hierarchical Bayesian probit models were discussed.

While the cost of crashes exceeds $1 Trillion a year in the U.S. alone, the availability of high-resolution naturalistic driving data provides an opportunity for researchers to conduct an in-depth analysis of crash contributing factors, and design appropriate interventions. Although police-reported crash data provides information on crashes, this study takes advantage of the SHRP2 Naturalistic Driving Study (NDS) which is a unique dataset that allows new insights due to detailed information on driver behavior in normal, pre-crash, and near-crash situations, in addition to trip and vehicle performance characteristics. This paper investigates the role of pre-crash driving instability, or driving volatility, in crash intensity (measured on a 4-point scale from a tire-strike to an injury crash) by analyzing microscopic vehicle kinematic data. NDS data are used to investigate not only the vehicle movements in space but also the instability of vehicles prior to the crash and their contribution to crash intensity using path analysis. A subset of the data containing 617 crash events with around 0.18 million temporal trajectories are analyzed. To quantify driving instability, microscopic variations or volatility in vehicular movements before a crash are analyzed. Specifically, nine measures of pre-crash driving volatility are calculated and used to explain crash intensity. While most of the measures are significantly correlated with crash intensity, substantial positive correlations are observed for two measures representing speed and deceleration volatilities. Modeling results of the fixed and random parameter probit models revealed that volatility is one of the leading factors increasing the probability of a severe crash. Additionally, the speed prior to a crash is highly correlated with intensity outcomes, as expected. Interestingly, distracted and aggressive driving are highly correlated with driving volatility and have substantial indirect effects on crash intensity. With volatile driving serving as a leading indicator of crash intensity, given the crashes analyzed in this study, early warnings and alerts for the subject vehicle driver and proximate vehicles can be helpful when volatile behavior is observed.

Lane Departure Warning (LDW) is a production active safety system that can warn drivers of an unintended departure. Critical in the design of LDW and other departure countermeasures is understanding pre-crash driver behavior in crashes. The objective of this study was to gain insight into pre-crash driver behavior in departure crashes using Event Data Recorders (EDRs). EDRs are units equipped on many passenger vehicles that are able to store vehicle data, including pre-crash data in many cases. This study used 256 EDRs that were downloaded from GM vehicles involved in real-world lane departure collisions. The crashes were investigated as part of the NHTSA's NASS/CDS database years 2000 to 2011. Nearly half of drivers (47%) made little or no change to their vehicle speed prior to the collision and slightly fewer decreased their speed (43%). Drivers who did not change speed were older (median age 41) compared to those who decreased speed (median age 27). Drivers in lane departure crashes were traveling above the posted speed limit in 65% of cases. Almost half (49%) of drivers did not apply the brakes during the pre-crash record, suggesting that drivers may be steering more often than braking to avoid crashes. The newest advanced EDRs record steering wheel angle in addition to the data collected by older EDRs most common in the current fleet. We also present a case study using advanced EDR data to simulate the vehicle's trajectory. This method could be invaluable in benefits estimates of safety systems. Language: en

Driving is one of the conditions when the driver should be given special attention to make the motor vehicle in driving way and the driver should be in a good condition. However, the problem is in a certain condition the driver lost their control speed due to their target and minimize the driving time and it led the high potential to accident. Therefore, speed limiter which applied in the bus is urgently needed to reduce the accident and improve their awareness of road safety. Developed speed limiter is completed by fuel cut-off system to prevent the engine and maintain the speed. Limitation of speed be adapted by government regulation. From the results show that the highest and average speed of 136 km/h and 123.5 km/h is observed by bus speed prior to use speed limiter. After speed limiter applied in the bus, the approved maximum speed is 90 km/h. Those data approve that the speed limiter can reduce 83% from the top speed before speed limiter applied.

This paper introduces the advantage and disadvantage of traditional motion compensation way in the stepped-frequency Pulses radar, and analyzes the motion influence on the one-dimension range profile. On the premise of understanding the current speed compensation algorithm, this paper offers an improved method, which is the new two-step approach. This method reduces the operation and enhances the Real-time performance of system. The characteristic of this method is that proposes the concept of the minimum pulse group self adaptive speed researching range. Even though, we don’t have the premise of speed prior knowledge, it also has a well applying prospect and universality. Comparing to the algorithm previously advanced, it has more extensive applications and practicability.

Observers viewed visual stimuli in which one object moved to a position of partial occlusion by another. The objects were presented as two-dimensional profiles moving in an undefined space, so the partial occlusion supports several different physical interpretations. In fact some stimuli reliably gave rise to a perceptual impression that the moving object penetrated or pierced the stationary one. This kind of interaction impression has not previously been reported. The impression was maximized by rapid deceleration to a halt with minimal occlusion. If the object decelerated more slowly, so that it was completely occluded or projected from the far side of the stationary object, it was perceived as moving behind the stationary object. The shape of the moving object and its speed prior to occlusion had significant but small effects.

Schools of herring exposed to progressive hypoxia show a peak in velocity during severe hypoxia, at 15-34% oxygen saturation, followed by a decrease in swimming speed until school disruption occurred. The observed increase in swimming speed during severe hypoxia reveals a graded response, since the lower the fish's swimming speed prior to severe hypoxia (U 95-50 , the speed at oxygen saturations between 95 and 50%), the greater the relative increase in swimming speed. The oxygen saturations at which both peak velocity and school disruption occurred were lower for fish with lowest U 95-50 , suggesting that the fish with the slowest speed U 95 50 reach their critical P O2 (at which there is respiratory distress) last, i.e. at lower oxygen saturation. At a functional level it is suggested that herring encountering hypoxia increase their speed in order to find more favourable conditions, and the magnitude of this increase is modulated by their respiratory distress. It is also hypothesised that the observed increase in speed may be related to an increase in the rate of position shifting within the school. Since the oxygen saturation at which the response to hypoxia occurs and the magnitude of the response are related to the fish's preferred speed prior to severe hypoxia, it is suggested that such a preferred speed should be measured in experiments testing the effect of hypoxia on fish behaviour.

This paper develops a probabilistic model for analyzing longitudinal collision/safety between an abruptly decelerating vehicle and its immediate follower. The input parameters are the length of the gap between the two vehicles their common speed prior to the failure. the reaction delav of the followinr vehicle and a bivaria~e dismbution for the two dmleration rates. The o;tput includes ~h;~robabilit~ of a collision and the probability distribution of the relative speed at collision time. We use this model to compare the safety consequences associated with the platooning and "free-agent" longitudinal-separation rules We also demonstrate that the free-agent rule implemented with a potential technology of fast and accurate emergency deceleration, under some reasonable conditions, can avoid collisions while offering a high freeway capacity previously thought possible only under the platooning rule. This model has many other applications

General intelligence requires open-ended exploratory learning. The principle of compression progress proposes that agents should derive intrinsic reward from maximizing “interestingness”, the first derivative of compression progress over the agent’s history. Schmidhuber posits that such a drive can explain “essential aspects of ... curiosity, creativity, art, science, music, [and] jokes”, implying that such phenomena might be replicated in an artificial general intelligence programmed with such a drive. I pose two caveats: 1) as pointed out by Rayhawk, not everything that can be considered “interesting” according to this definition is interesting to humans; 2) because of (irrational) hyperbolic discounting of future rewards, humans have an additional preference for rewards that are structured to prevent premature satiation, often superseding intrinsic preferences for compression progress. Consider an agent operating autonomously in a large and complex environment, absent frequent external reinforcement. Are there general principles the agent can use to understand its world and decide what to attend to? It has been observed going back to Leibniz that understanding is in many respects equivalent to compression. To understand its world, a competent agent will thus attempt, perhaps implicitly, to compress its history through the present, consisting of its observations, actions, and external rewards (if any). Any regularities that we can find in our history through time t, h(≤ t), may be encoded in a program p that generates the data h(≤ t) as output by exploiting said regularities. Schmidhuber has proposed the principle of compression progress (Sch09): long-lived autonomous agents that are computationally limited should be given intrinsic reward for increasing subjective “interestingness”, defined as the first derivative of compression progress (compressing h(≤ t)). Agents that are motivated by compression progress will seek out and focus on regions of their environment where such progress is expected. They will avoid both regions of the world which are entirely predictable (already highly compressed), and entirely unpredictable (incompressible and not expected to yield to compression progress). Cf. (Bau04) for a modern formulation of this argument. A startling application of the principle of compression progress is to explain “essential aspects of subjective beauty, novelty, surprise, interestingness, attention, curiosity, creativity, art, science, music, jokes”, as attempted in (Sch09). The unifying theme in all of these activities, it is argued, is the active process of observing new data which provide for the discovery of novel patterns. These patterns explain the data as they unfold over time by allowing the observer to compress it more and more. This progress is explicit and formal in science and mathematics, while it may be implicit and even unconscious in art and music. To be clear, engaging in these activities often provides external rewards (fame and fortune) that are not addressed here; we consider only the intrinsic rewards from such pursuits. Rayhawk (Ray09) criticizes this attempt with a gedankenexperiment. First, generate a (long) sequence of 2 bits with a psuedorandom number generator (PRNG) using an unknown but accessible random seed, n bits long. Assuming that the PRNG is of high quality and our agent is computationally limited, such a sequence will require Θ(2) bits to store. Access the random seed, and use it to recode the original 2 bits in Θ(n) space by storing just the seed and the constantlength PRNG code. This will lead to compression progress, which can be made as large as we would like by increasing n. Of course, such compression progress would be very uninteresting to most people! The applicability of this procedure depends crucially on two factors: 1) how the complexity of compression programs is measured by the agent, namely the tradeoff between explanation size (in bits) and execution time (in elementary operation on bits); and 2) which sorts of compression programs may be found by the agent. Consider an agent that measures compression progress between times t and t+ 1 by C(p(t), h(≤ t+1))−C(p(t+1), h(≤ t+1)) (see (Sch09) for details). Here p(t) is the agent’s compression program at time t, and C(p(t), h(≤ t+ 1) is the cost to encode the agent’s history through time t+ 1, with p(t). If execution time is not accounted for in C (i.e. cost is simply the length of the compressor program), and p may be any primitive recursive program, the criticism disappears. This is because even without knowing the random seed, O(n) Published by Atlantis Press, © the authors 1 bits are sufficient to encode the sequence, since we can program a brute-force test of all possible seeds without incurring any complexity costs, while storing only a short prefix of the overall sequence. Thus, the seed is superfluous and provides no compression gain. If execution time has logarithmic cost relative to program size, as in the speed prior (Sch02), then learning the seed will provide us with at most a compression gain logarithmic in n. This is because testing all random seeds against a prefix of the the sequence takes O(n2) time, so C(p(t), h(≤ t + 1)) will be about n + log(n), while C(p(t+ 1), h(≤ t+ 1)) will be about n. Thus, such pathological behavior will certainly not occur with a time-independent prior. Unfortunately, the compression progress principle is intended for precisely those computationally limited agents with timedependent priors, that are too resource-constrained to brute-force random seeds. A reasonable alternative is to posit an a priori weighting over data that would assign zero utility to compression progress on such a sequence, and nonzero utility to compression of e.g. knowledge found in books, images of human faces, etc. This gives a principle of weighted compression progress that somewhat less elegant, but perhaps more practical. A very different theory that also addresses the peculiar nature of intrinsic rewards in humans is hyperbolic discounting, based on long-standing results in operant conditioning (Her61). In standard utility theory, agents that discount future rewards against immediate rewards do so exponentially; an expected reward occurring t units of time in the future is assigned utility rγ relative to its present utility of r, where γ is a constant between 0 and 1. The reason for the exponential form is that any other function leads to inconsistency of temporal preferences; what the agent prefers now will not be what it prefers in the future. However, considerable empirical evidence (Ain01) shows that humans and many animals discount future reward not exponentially, but hyperbolically, approximating r(1 + t)−1. Because of the hyperbolic curve’s initial relative steepness, agents discounting according to this formula are in perpetual conflict with their future selves. Immediately available rewards can dominate decision-making to the detriment of cumulative reward, and agents are vulnerable to selfinduced “premature satiation”, a phenomenon that is nonexistent in exponential discounters (Ain01). While an exponential discounter may prefer a smaller sooner reward (when γ < 1), this preference will be entirely consistent over time; there will be no preference reversal as rewards become more imminent. Hyperbolic discounting and the compression progress principle intersect when we consider activities that provide time-varying intrinsic rewards. They conflict when rewards may be consumed at varying rates for varying amounts of total reward. Consider an agent examining a complex painting or sculpture that is not instantaneously comprehensible, but must be understood sequentially through a series of attention-shifts to various parts. Schmidhuber (Sch09) asks: “Which sequences of actions and resulting shifts of attention should he execute to maximize his pleasure?” and answers “According to our principle he should select one that maximizes the quickly learnable compressibility that is new, relative to his current knowledge and his (usually limited) way of incorporating / learning / compressing new data.” But a hyperbolically discounting agent is incapable of selecting such a sequence voluntarily! Due to temporal skewing of action selection, a suboptimal sequence that provides more immediate rewards will be chosen instead. I posit that the experiences humans find most aesthetically rewarding are those with intrinsic reward, generated by weighted compression progress, that are structured to naturally prevent premature satiation. In conclusion, I posit two major qualifications of the applicability of the principle of compression progress to humans. First, that the value of compression progress is weighted by the a priori importance of the data that are being compressed. This is most obvious in our interest in faces, interpersonal relations, etc. Even more abstract endeavors such as music (Mit06) and mathematics (LN01) are grounded in embodied experience, and only thus are such data worth compressing to begin with. Second, that experiences that intrinsically limit the “rate of consumption” of compression progress will be preferred to those requiring self-regulated consumption, even when less total reward is achievable by a rational agent in the former case than in the latter. AGI designers should bear these caveats in mind when constructing intrinsic motivations for their agents. Acknowledgements Thanks to Steve Rayhawk and Jurgen Schmidhuber for helpful discussion.

Collision of automobile wheels with a vertical obstacle as a result of which the wheel tire gets dismantled is considered in the present article. The required force and the minimal speed of driving which is equivalent to the expenditures of the kinetic energy necessary for dismantling of the tire from the wheel rim after the collision with an obstacle are ascertained. The values of this force and speed typical of automobiles attributed to different classes, their dependence upon the height of the obstacle and the angle of collision with the obstacle are fixed. A more precise method for the calculation of the automobile speed prior to the traffic accident taking into consideration the expenditures of the kinetic energy necessary for dismantling of the wheel after the occurrence of its collision with a vertical obstacle is suggested. First Published Online: 27 Oct 2010

Describes a relatively straightforward method of estimating the probability of loss of separation between two aircraft at some future time, that is, the probability that they will enter into conflict. Initially the method is developed for encounters in which the planned or predicted speeds of both aircraft are constant during the prediction interval and in which their tracks are straight in regions of possible conflict. Various graphs of conflict probability are produced which are comparable with those already reported. These graphs indicate the levels of performance that may be attainable with medium-term conflict detectors. The method can also be applied in the cases of planned changes in speed prior to entering regions of possible conflict, and also to position-based changes in heading within regions of possible conflict, making it more suitable for use in conflict detectors.

Solomonoff's optimal but noncomputable method for inductive inference assumes that observation sequences x are drawn from an recursive prior distribution µ(x). Instead of using the unknown µ(x) he predicts using the celebrated universal enumerable prior M(x) which for all x exceeds any recursive µ(x), save for a constant factor independent of x. The simplicity measure M(x) naturally implements "Occam's razor" and is closely related to the Kolmogorov complexity of x. However, M assigns high probability to certain data x that are extremely hard to compute. This does not match our intuitive notion of simplicity. Here we suggest a more plausible measure derived from the fastest way of computing data. In absence of contrarian evidence, we assume that the physical world is generated by a computational process, and that any possibly infinite sequence of observations is therefore computable in the limit (this assumption is more radical and stronger than Solomonoff's). Then we replace M by the novel Speed Prior S, under which the cumulative a priori probability of all data whose computation through an optimal algorithm requires more than O(n) resources is 1/n. We show that the Speed Prior allows for deriving a computable strategy for optimal prediction of future y, given past x. Then we consider the case that the data actually stem from a nonoptimal, unknown computational process, and use Hutter's recent results to derive excellent expected loss bounds for S-based inductive inference. We conclude with several nontraditional predictions concerning the future of our universe.