Abstract <p>The heterogeneity and feasibility of test scenarios are crucial factors in controlled experiments examining the performance of autonomous vehicles. The challenges in constructing a test site or outdoor laboratory for such experiments lie in efficiently and effectively planning and testing the critical test scenarios. This paper proposes a heuristics path planning method based on the rapidly exploring random-tree algorithm for solving the challenge mentioned above. To demonstrate this new path planning method, a case study is conducted at the outdoor laboratory of Suzhou Automotive Research Institute, Tsinghua University, China. The results show that the new path planning method not only allows more test scenarios to be implemented but also completes all of the necessary experiments within the shortest total experimental time or mileage. Hence, the new path planning method saves costs, increases the efficiencies of controlled experiments, and accelerates the scenario testing of autonomous vehicles in closed-form test sites.</p>