Multi-modal explainable artificial intelligence (M-XAI) refers to the field of study that focuses on making sure that people understand the reasoning behind decisions made by machines. In simpler terms, M-XAI should produce explanations for AI findings that are plain and straightforward enough for human beings to comprehend with their limited knowledge of algorithms. This means that it is necessary to open up some parts of an opaque system if we want others to trust us, expose everything so that everybody can be held responsible, and let individuals cooperate among themselves as well as between them and artificial intelligence systems. In order to increase awareness both within individuals as well as organisations about responsible use and adoption while fostering local/global development on the same note, considering its potential influence across many industries like transportation, where intelligent transport systems have recently been introduced, leading to a new era characterised by high levels of efficiency combined with minimal congestion rates along major highways. This study scrutinises the utilisation of this (M-XAI) explainable artificial intelligence concept of self-driving independent vehicles. For instance, it analyses (M-XAI) approaches designed for self-driving cars and evaluates their strengths and weaknesses across different tasks. In addition to highlighting problems related to explainability here, we also suggest research areas that need more exploration.