关键词: 交通运输工程；自动驾驶；切入场景；参数优化；安全测试；加速测试

Abstract: Highway cut-in scenarios can easily cause rear-end collisions and traffic flow disturbances, which is a key scenario for autonomous driving safety testing. Test parameters are usually directly sampled within a wide range, with initial spacing, speed difference, and lane changing time each bounded and combined, ignoring their kinematic coupling. The large number of samples that cannot be achieved or are far from the safe haven boundary dilutes the testing budget. A parameter optimization method for highway cut-in scenarios was proposed. Firstly, by combining horizontal accessibility with vertical relative motion, a physical support domain was constructed to eliminate parameter combinations that were not feasible or had unreasonable interactions. Subsequently, normalized safety margins based on vehicle response and braking capabilities were constructed, characterizing the proximity of parameter points to the safe haven boundary. Finally, the risk density within the critical region was estimated and the compact non-rectangular parameter subdomains was screened out through equivalent set filtering. Given the vehicle capability parameters and finite time domain kinematic discriminator, this subdomain occupied 4.35% of the global parameter space, and the collision sample rate increased from 31.09% of the full parameter space sampling to 86.76%. It was found that the average number of tests for a collision sample decreased from 3.22 to 1.15. Under 36 sets of grids and smoothing settings, feasible regions were obtained, and the collision rate was verified to be between 85.5% and 87.9%. Exploratory analysis of highD trajectories in the dataset shows that there is also similar enrichment in natural driving, that is, sorted by simulated risk density, the proportion of danger in the top 25% of samples is 13.64%, which is 3.41 times that of random testing. This proposed method is located upstream of scenario sampling, and the output can be directly used as parameter input for methods such as importance sampling.

Key words: traffic and transportation engineering; autonomous driving; cut-in scenario; parameter optimization; safety testing; accelerated testing