A Systematic Literature Review about the impact of Artificial Intelligence on Autonomous Vehicle Safety <s> LTL <s> The final purpose of Automated Vehicle Guidance Systems (AVGSs) is to obtain fully automatic driven vehicles to optimize transport systems, minimizing delays, increasing safety and comfort. In order to achieve these goals, lots of Artificial Intelligence techniques must be improved and merged. In this article we focus on learning and simulating the Human-Level decisions involved in driving a racing car. To achieve this, we have studied the convenience of using Neuroevolution of Augmenting Topologies (NEAT). To experiment and obtain comparative results we have also developed an online videogame prototype called Screaming Racers, which is used as test-bed environment. <s> BIB001 </s> A Systematic Literature Review about the impact of Artificial Intelligence on Autonomous Vehicle Safety <s> LTL <s> Researchers and automotive industry experts are in favour of accelerating the development of “cognitive vehicle” features, which will integrate intelligent technology and human factors for providing non-distracting interface for safety, efficiency and environmental sustainability in driving. In addition, infotainment capability is a desirable feature of the vehicle of the future, provided that it does not add to driver distraction. Further, these features are expected to be a stepping-stone to fully autonomous driving. This paper describes advances in driver-vehicle interface and presents highlights of research in design. Specifically, the design features of the cognitive vehicle are presented and measures needed to address major issues are noted. The application of Bayesian Artificial Intelligence is described in the design of driving assistance system, and design considerations are advanced in order to overcome issues in in-vehicle telematics systems. Finally, conclusions are advanced based on coverage of research material in the paper. <s> BIB002
The process of test-case derivation can be applied was demonstrated. According to the authors, the derivation of test-cases categorizes the recordings automatically and allows test engineers to specify test inputs. The test-case descriptions use the Linear Temporal Logic (LTL) and allow the execution of continuous behaviors, which may also react to the behavior of the tested vehicle. According to the authors, as the traffic recordings can also be used for machine learning algorithms, the contributes to the discussion of their safety certification. They also state the approach is flexible as it can be extended if new traffic situations are supposed to be covered by testing. No specific findings, only discussions about the important considerations to be taken into account when designing AVs BIB002 overcome issues in invehicle telematics systems Integration between AV and human driver un-obstructive human driver monitoring approaches to ensure they will be able to take over control within short notice General AI/ML One of the most essential parts of the autonomous driving system is to monitor driver's physical and mental state to avoid unexpected traffic accidents. Low-cost remote-controlled car prototype with basic automated functions and using basic off-the-shelf components with promising results. It proved cheap and useful prototypes can be built for research. Results with the proposed road detection techniques proved to be highly efficient for a road with clearly visible lane market. Canny Edge detection proved to have low error rate.  Selective Attention Mechanism Weber-Fechner law Regression Based Models Besides, the model is consistent with the famous Weber-Fechner law. The WeberFechner law says that all people's feeling, including visual feeling, auditory feeling and so on all comply with the fact that the feeling is not proportional to the strength of the corresponding physical quantity but proportional to the logarithm of the corresponding physical quantity. Pilots' learning curve is irregular, due to the characteristics of the problem, but presents a good positive tendency which BIB001 decisions involved in driving a racing car a online videogame prototype as a test-bed environment. NEAT is a combination of ANN and Genetic Algorithm (GA) combined to other techniques leads them to acquire fruitful abilities in just 50 generations with a population of 120 individuals. Pilots easily learn how to turn following soft cruves, but still have big poblems ientifying and steering hard ones, which even make them crash into track limits sometimes. The paper presents individuals habing only 2 output neurons: one for turning and the other one for throttling/braking. Therefore, for this reatively young ANNs, is very dificult to acquire the high-level behaviours that have to completely change the sign of the output of the second neuron, whenevver a sharp curve is near. AV experiment (100 km) AV following a manually driven car (trailing)