Hierarchical　reinforcement　learning　(HRL)　has　demonstrated　considerable　promise　in　addressing　complex　driving　tasks.　However,　existing　HRL-based　autonomous　driving　decision　systems　face　challenges　such　as　inefficient　convergence,　lack　of　interdependence　among　driving　maneuver　strategies　(including　throttle/brake　control　and　steering　adjustments),　and　inadequate　risk　assessment　mechanisms,　all　of　which　impede　the　safety　and　stability　of　lane-changing　decisions.　This　study　proposes　a　novel　HRL　framework　for　continuous　lane-changing　decision　planning.　This　framework　establishes　cascaded　relationships　between　driving　maneuvers　strategies　and　integrates　a　comprehensive　risk　assessment　mechanism　to　address　these　challenges.　Initially,　a　hierarchical　decision　model　is　developed,　where　the　high-level　determines　the　lane-changing　intent,　while　the　low-level　manages　continuous　and　precise　maneuvers.　Subsequently,　by　integrating　a　Bayesian　network,　the　cascading　between　throttle/brake　openings　and　steering　angles　is　achieved,　optimizing　the　system＇s　joint　strategy　distribution.　Furthermore,　a　comprehensive　risk　assessment　mechanism　that　evaluates　the　cooperation　level　of　drivers　and　the　severity　of　potential　collisions　is　designed　to　encourage　agents　to　adopt　strategies　that　minimize　risk.　The　effectiveness　of　the　proposed　decision-making　framework　has　been　validated　through　comparative　experiments　in　mixed　traffic　scenarios　simulated　within　the　Car　Learning　to　Act　(CARLA)　environment　and　corroborated　with　human　driving　data　from　the　Next　Generation　Simulation　(NGSIM)　database.　©　2025　Elsevier　Ltd