Accurately　detecting　and　predicting　Lane　Change　(LC)　processes　of　human-driven　vehicles　can　help　autonomous　vehicles　better　understand　their　surrounding　environment,　recognize　potential　safety　hazards,　and　improve　traffic　safety.　This　paper　focuses　on　LC　processes,　first　developing　a　Temporal　Convolutional　Network　(TCN)　with　an　attention　mechanism　(ATM)　model　to　recognize　LC　intention.　Then,　considering　the　intrinsic　relationship　among　output　variables,　the　Multi-Task　Learning　(MTL)　framework　is　employed　to　simultaneously　predict　multiple　LC　vehicle　status　indicators.　Furthermore,　a　unified　modeling　framework　for　LC　intention　recognition　and　driving　status　prediction　(LC-IR-SP)　is　developed.　The　results　indicate　that　the　classification　accuracy　of　LC　intention　was　improved　from　95.83%　to　98.20%　when　incorporating　the　ATM　into　the　TCN　model.　For　LC　vehicle　status　prediction　issues,　Pearson＇s　correlation　coefficient　indicates　that　metrics　extracted　from　LC　processes　show　stronger　correlation　than　those　extracted　from　Lane-keeping　processes.　Consequently,　three　multi-tasking　learning　models　are　constructed　based　on　the　MTL　framework.　The　results　indicate　that　the　MTL　with　Long　Short-Term　Memory　(MTL-LSTM)　model　outperforms　the　MTL　with　TCN　(MTL-TCN)　and　MTL　with　TCN-ATM　(MTL-TCN-ATM)　models.　Compared　to　the　corresponding　single-task　model,　the　MTL-LSTM　model　demonstrates　an　average　decrease　of　26.04%　in　MAE　and　25.19%　in　RMSE.　The　LC-IR-SP　model　developed　holds　great　potential　in　enhancing　autonomous　vehicles＇　perception　and　prediction　capabilities,　such　as　identifying　LC　behaviors,　calculating　real-time　traffic　conflict　indices,　and　improving　vehicle　control　strategies.