Multi-object tracking and road feature detection are critical perception tasks in Advanced Driver Assistance Systems (ADAS), as they directly affect perception robustness and driving safety. This paper proposes algorithms for target tracking and speed bump detection based on solid-state LiDAR point clouds. For multi-object tracking, an Extended Kalman Filter (EKF) with a Constant Turn Rate and Velocity (CTRV) motion model is used to estimate full target states including position, velocity, heading, and yaw rate. By performing inter-frame data association and state estimation on solid-state LiDAR data, the method enables robust tracking of dynamic targets. A Markov chain–based track management strategy ensures reliable initiation, maintenance, and termination of tracks in complex traffic environments.