Mobile robot path planning has passed through multiple phases of development and took up several challenges. Up to now and with the new technology in hands, it becomes less complicated to conduct path planning for mobile robots and avoid both static and dynamic obstacles, so that collision-free navigation is ensured. Thorough state of the art review analysis with critical scrutiny of both safe and optimal paths for autonomous vehicles is addressed in this study. Emphasis is given to several developed techniques based using sampling algorithms, node-based optimal algorithms, mathematic model-based algorithms, bio-inspired algorithms, which includes neural network algorithms, and then multi-fusion-based algorithms, which combine different methods to overcome the drawbacks of each. All of these approaches consider different conditions and they are used for multiple domains.

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As self-driving cars perform more tasks, new challenges arise. One of these challenging tasks is autonomous driving decision-making due to the uncertainty of the vehicle’s complex environment. This paper provides an overview of decision-making technology and trajectory control for autonomous vehicles. The main common goal in decision-making is to consider uncertainties, unpredictable situations, and driving tasks to propose a global and robust solution adapted to each situation. The main concern is safety. Decision-making falls into three categories. The first is the traditional approach, which often consists of building a rule system and deriving optimal operations. The advantages of such an approach are well known for being easy to understand and applicable to small problems. The second category of decision-making is based on a probabilistic process and, due to its efficiency, has several applications in this area. The third category is learning-based approaches. Once a decision has been made, manipulate the steering angle or accelerator/brake pedals to perform the appropriate action. Two approaches are existing to designing autonomous driving controllers. Either based on imitating human drivers that includes approaches based on the use of driver models such as AI, or the use of approach-based models