In this paper, we develop a new algorithm, called T$^{star}$-Lite, that enables fast time-risk optimal motion planning for variable-speed autonomous vehicles. The T$^{star}$-Lite algorithm is a significantly faster version of the previously developed T$^{star}$ algorithm. T$^{star}$-Lite uses the novel time-risk cost function of T$^{star}$; however, instead of a grid-based approach, it uses an asymptotically optimal sampling-based motion planner. Furthermore, it utilizes the recently developed Generalized Multi-speed Dubins Motion-model (GMDM) for sample-to-sample kinodynamic motion planning. The sample-based approach and GMDM significantly reduce the computational burden of T$^{star}$ while providing reasonable solution quality. The sample points are drawn from a four-dimensional configuration space consisting of two position coordinates plus vehicle heading and speed. Specifically, T$^{star}$-Lite enables the motion planner to select the vehicle speed and direction based on its proximity to the obstacle to generate faster and safer paths. In this paper, T$^{star}$-Lite is developed using the RRT$^{star}$ motion planner, but adaptation to other motion planners is straightforward and depends on the needs of the planner
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