Blockchain-based　Federated　Learning　(FL)　technology　enables　vehicles　to　make　smart　decisions,　improving　vehicular　services　and　enhancing　the　driving　experience　through　a　secure　and　privacy-preserving　manner　in　Intelligent　Transportation　Systems　(ITS).　Many　existing　works　exploit　two-layer　blockchain-based　FL　frameworks　consisting　of　a　mainchain　and　subchains　for　data　interactions　among　intelligent　vehicles,　which　resolve　the　limited　throughput　issue　of　single　blockchain-based　vehicular　networks.　However,　the　existing　two-layer　frameworks　still　suffer　from　a)　strong　dependency　on　predetermined　and　fixed　parameters　of　vehicular　blockchains　which　limit　blockchain　throughput　and　reliability;　and　b)　high　communication　costs　incurred　by　interactions　among　intelligent　vehicles　between　the　mainchain　and　subchains.　To　address　the　above　challenges,　we　first　design　an　adaptive　blockchain-enabled　FL　framework　for　ITS　based　on　blockchain　sharding　to　facilitate　decentralized　vehicular　data　flows　among　intelligent　vehicles.　A　streamline-based　shard　transmission　mechanism　is　proposed　to　ensure　communication　efficiency　almost　without　compromising　the　FL　accuracy.　We　further　formulate　the　proposed　framework　and　propose　an　adaptive　sharding　mechanism　using　Deep　Reinforcement　Learning　to　automate　the　selection　of　parameters　of　vehicular　shards.　Numerical　results　clearly　show　that　the　proposed　framework　and　mechanisms　achieve　adaptive,　communication-efficient,　credible,　and　scalable　data　interactions　among　intelligent　vehicles.