The　modern　intelligent　transportation　system　opts　for　accident　prediction　modules　as　a　critical　aspect　for　road　safety.　Where,　an　accident　is　predicted　before　it　actually　happens　and　precautionary　measures　be　taken　for　its　avoidance.　Accident　prediction　methods＇　analysis　is　also　popular　for　critical　infrastructure　protection.　Recently,　Deep　Learning　(DL)　techniques　coupled　with　the　classical　Artificial　intelligence　(AI)　methods　have　produced　promising　results　in　various　domains.　This　work　presents　a　DL　and　AI-based　system　of　identifying　driving　risks　for　the　Light　Transport　Vehicles　(LTVs)　that　generate　early　warnings　before　an　anticipated　accident.　These　warnings　enable　to　avoid　critical　traffic　accidents.　The　proposed　system　collects　and　learns　various　driver＇s　LTV　driving　style　patterns　and　afterward　applies　DL　to　classify　the　driver　in　one　of　the　predefined　classes.　The　proposed　system　consists　of　a　custom-built　driving　simulator　integrated　with　a　computer　that　provides　real-life　driving　experience　for　data　recording.　The　miniature　hardware　simulator　mimics　an　LTV　consisting　of　a　steering　wheel,　clutch,　brake　accelerator,　gear　assembly,　and　indicators.　The　collected　data　is　first　evaluated　using　unsupervised　learning　methods.　Afterwards,　it　is　used　to　train　the　DL　classifier.　The　obtained　results　show　that　the　proposed　system　attains　an　average　classification　accuracy　of　85%,　where　the　maximum　accuracy　of　97%　is　observed　for　the　gradient　boosting　classifier.