The Dean-Kawasaki model consists of a nonlinear stochastic partial differential equation featuring a conservative, multiplicative, stochastic term with non-Lipschitz coefficient, and driven by space-time white noise; this equation describes the evolution of the density function for a system of finitely many particles governed by Langevin dynamics. Well-posedness for the Dean-Kawasaki model is open except for specific diffusive cases, corresponding to overdamped Langevin dynamics. There, it was recently shown by Lehmann, Konarovskyi, and von Renesse that no regular (non-atomic) solutions exist.We derive and analyse a suitably regularised Dean-Kawasaki model of wave equation type driven by coloured noise, corresponding to second order Langevin dynamics, in one space dimension. The regularisation can be interpreted as considering particles of finite size rather than describing them by atomic measures. We establish existence and uniqueness of a solution. Specifically, we prove a highprobability result for the existence and uniqueness of mild solutions to this regularised Dean-Kawasaki model. Proposition 1.1 (Tightness of {ρ } , {j } , {j 2, } ). Let T > 0, and let D ⊂ R be a bounded domain. Assume the validity of either Assumption (G) or Assumption (NG), given below in Subsection 2.2. Then the families of processes of {ρ } , {j } are tight in C(0, T ; L 2 (D)) and C(0, T ; L 4 (D)), respectively, for N θ ≥ 1, with θ ≥ 3. In addition, the family {j 2, } is tight in C(0, T ; L 4 (D)) for N θ ≥ 1, with θ ≥ 5. Proposition 1.1 yields relative compactness in law for the families of processes {ρ } , {j } , {j 2, } as → 0. We show convergence for the family {ρ } as → 0 in the following result. Proposition 1.2. Let T > 0, and let D ⊂ R be a bounded domain. Assume the validity of either Assumption (G) or Assumption (NG), as well as the scaling N θ ≥ 1, for some θ ≥ 3. For each > 0, let η be the law of the process ρ on X := C(0, T ; L 2 (D)). There exists a probability measure η on X such that η w → η in X as → 0. Here w → denotes weak convergence of measures.The proofs of Proposition 1.1 and 1.2 under Assumption (G) are the content of Subsection 3.1.