Path-speed decomposition-based trajectory planning schemes have garnered widespread usage in real-world robotics applications due to their efficacy and computational efficiency. While a global route can be planned offline, generating a local path adaptive to real-time situations online remains essential. We propose a local path planning algorithm that prioritizes smoothness and low computational complexity, facilitating scalability to dense environments with various on- road entities. Our algorithm leverages a sparse graph structure to generate crucial obstacle-specific nodes and connect them via spline edges. Several modifications are introduced to maintain graph sparsity, boosting computational efficiency without com- promising performance. The final path evaluation comprises consideration of path smoothness as well as risk pertaining to vulnerable road users. The effectiveness of the proposed algorithm is demonstrated through CARLA simulation studies and extensive comparative analysis against benchmarking methods. Finally, a scaled car demonstration is presented to showcase the performance of the proposed method on a physical system