Distribution of Damages in Car Accidents throught the Use of Neural Networks

After a traffic accident the damage has to be fairly divided among the parties involved, and a ratio has to be determined. There are many precedents for this, and judges have developed catalogues suggesting ratios for common types of accidents. The problem that "every case is different," however, remains. Many cases have familiar aspects, but also unfamiliar ones. Even if a case is composed of several familiar aspects with established ratios, the question remains as to how these are to be figured into one ratio. The first thought would be to invent a mathematical formula, but such formulae are rigid and speculative. The body of law has grown organically and must not be forced into a sleek system. The distant consequences of using a mathematical formula cannot be foreseen; they might well be grossly unjust. I suggest using a neural network instead. Precedents may be fed into the network directly as learning patterns. This has the advantage that court rulings can be transferred directly and not via a formula. Future modifications in court rulings also can be adopted by the network. As far as the effect of the learning patterns on new cases is concerned, a relatively safe assumption is that they will fit in harmoniously with the precedents. This is due to the network's structure—a number of simple decisional units, which are interconnected, tune their activity to each other, thus achieving a state of equilibrium. When the conditions of such an equilibrium are translated back into the terms of the case, the solution can hardly be totally unjust

High-Resolution Road Vehicle Collision Prediction for the City of Montreal

Road accidents are an important issue of our modern societies, responsible for millions of deaths and injuries every year in the world. In Quebec only, in 2018, road accidents are responsible for 359 deaths and 33 thousands of injuries. In this paper, we show how one can leverage open datasets of a city like Montreal, Canada, to create high-resolution accident prediction models, using big data analytics. Compared to other studies in road accident prediction, we have a much higher prediction resolution, i.e., our models predict the occurrence of an accident within an hour, on road segments defined by intersections. Such models could be used in the context of road accident prevention, but also to identify key factors that can lead to a road accident, and consequently, help elaborate new policies. We tested various machine learning methods to deal with the severe class imbalance inherent to accident prediction problems. In particular, we implemented the Balanced Random Forest algorithm, a variant of the Random Forest machine learning algorithm in Apache Spark. Interestingly, we found that in our case, Balanced Random Forest does not perform significantly better than Random Forest. Experimental results show that 85% of road vehicle collisions are detected by our model with a false positive rate of 13%. The examples identified as positive are likely to correspond to high-risk situations. In addition, we identify the most important predictors of vehicle collisions for the area of Montreal: the count of accidents on the same road segment during previous years, the temperature, the day of the year, the hour and the visibility

LIDAR-based Driving Path Generation Using Fully Convolutional Neural Networks

In this work, a novel learning-based approach has been developed to generate driving paths by integrating LIDAR point clouds, GPS-IMU information, and Google driving directions. The system is based on a fully convolutional neural network that jointly learns to carry out perception and path generation from real-world driving sequences and that is trained using automatically generated training examples. Several combinations of input data were tested in order to assess the performance gain provided by specific information modalities. The fully convolutional neural network trained using all the available sensors together with driving directions achieved the best MaxF score of 88.13% when considering a region of interest of 60x60 meters. By considering a smaller region of interest, the agreement between predicted paths and ground-truth increased to 92.60%. The positive results obtained in this work indicate that the proposed system may help fill the gap between low-level scene parsing and behavior-reflex approaches by generating outputs that are close to vehicle control and at the same time human-interpretable.Comment: Changed title, formerly "Simultaneous Perception and Path Generation Using Fully Convolutional Neural Networks

Compositional Falsification of Cyber-Physical Systems with Machine Learning Components

Cyber-physical systems (CPS), such as automotive systems, are starting to include sophisticated machine learning (ML) components. Their correctness, therefore, depends on properties of the inner ML modules. While learning algorithms aim to generalize from examples, they are only as good as the examples provided, and recent efforts have shown that they can produce inconsistent output under small adversarial perturbations. This raises the question: can the output from learning components can lead to a failure of the entire CPS? In this work, we address this question by formulating it as a problem of falsifying signal temporal logic (STL) specifications for CPS with ML components. We propose a compositional falsification framework where a temporal logic falsifier and a machine learning analyzer cooperate with the aim of finding falsifying executions of the considered model. The efficacy of the proposed technique is shown on an automatic emergency braking system model with a perception component based on deep neural networks

Is swarm intelligence able to create mazes?

In this paper, the idea of applying Computational Intelligence in the process of creation board games, in particular mazes, is presented. For two different algorithms the proposed idea has been examined. The results of the experiments are shown and discussed to present advantages and disadvantages