No abstract

Despite the great technological advances in ADAS, autonomous driving still faces many challenges. Among them is improving decision-making algorithms so that vehicles can make the right decision inspired by human driving. Not only must these decisions ensure the safety of the car occupants and the other road users, but they have to be understandable by them. This article focuses on decision-making algorithms for autonomous vehicles, specifically for lane changing on highways and sub-urban roads. The challenge to overcome is to develop a decision-making algorithm that combines fidelity to human behavior and that is based on machine learning, with a global structure that allows understanding the behavior of the algorithm and that is not opaque such as black box algorithms. To this end, a three-step decision-making method was developed: trajectory prediction of the surrounding vehicles, risk and gain computation associated with the maneuver and based on the predicted trajectories, and finally decision making. For the decision making, three algorithms: decision tree, random forest, and artificial neural network are proposed and compared based on a naturalistic driving database and a driving simulator.

No abstract

The last few years, the automotive industry sees the Autonomous Vehicles (AV) as a great opportunity to increase comfort and road safety. One of the most challenging tasks is to detect dangerous situations and react to avoid or, at least, mitigate accidents. This requires a prediction of the evolution of the traffic surrounding the vehicle. This paper is a survey of the methods used in Automotive engineering for predicting future trajectories and collision risk assessment. models of vehicles are classified from the simplest to the more complexes. These technologies aim to improve road safety by estimating the level of dangerousness of a situation to make decision to avoid collision or mitigate its consequences.