Predicting　the　future　trajectories　of　dynamic　traffic　actors　is　a　cornerstone　task　in　autonomous　driving.　Though　existing　notable　efforts　have　resulted　in　impressive　performance　improvements,　a　gap　persists　in　scene　cognitive　and　understanding　of　complex　traffic　semantics.　This　paper　proposes　Traj-LLM,　the　first　to　investigate　the　potential　of　using　pre-trained　Large　Language　Models　(LLMs)　without　explicit　prompt　engineering　to　generate　future　motions　from　vehicular　past　trajectories　and　traffic　scene　semantics.　Traj-LLM　starts　with　sparse　context　joint　encoding　to　dissect　the　agent　and　scene　features　into　a　form　that　LLMs　understand.　On　this　basis,　we　creatively　explore　LLMs'　strong　understanding　capability　to　capture　a　spectrum　of　high-level　scene　knowledge　and　interactive　information.　To　emulate　the　human-like　lane　focus　cognitive　function　and　enhance　Traj-LLM's　scene　comprehension,　we　introduce　lane-aware　probabilistic　learning　powered　by　the　Mamba　module.　Finally,　a　multi-modal　Laplace　decoder　is　designed　to　achieve　scene-compliant　predictions.　Extensive　experiments　manifest　that　Traj-LLM,　fueled　by　prior　knowledge　and　understanding　prowess　of　LLMs,　together　with　lane-aware　probability　learning,　transcends　the　state-of-the-art　methods　across　most　evaluation　metrics.　Moreover,　the　few-shot　analysis　serves　to　substantiate　Traj-LLM's　performance,　as　even　with　merely　50%　of　the　dataset,　it　surpasses　the　majority　of　benchmarks　relying　on　complete　data　utilization.　This　study　explores　endowing　the　trajectory　prediction　task　with　advanced　capabilities　inherent　in　LLMs,　furnishing　a　more　universal　and　adaptable　solution　for　forecasting　agent　movements　in　a　new　way.　IEEE