11 accepted papers
Rainy datasets play a critical role to train the neural networks for autonomous driving, and yet they are scarce when compared with datasets captured in the clean weather. Simulation works consisting of generating the rainy scenarios completely on engine and inserting raindrops into the real sunny s
Federated Quantile Regression (FQR) has emerged as a powerful modelling paradigm for estimating conditional quantiles, offering a more comprehensive understanding of response distributions than standard conditional mean regression. However, achieving communication efficiency and optimal statistical …
Along-edge driving, where an autonomous vehicle follows road edges, is increasingly common in urban environments and particularly challenging on curvy roads due to rapidly changing curvature. This paper presents a hierarchical trajectory planning framework that integrates Cartesian and Frenet frames
Randomized response (RR) mechanisms constitute a fundamental and effective technique for ensuring label differential privacy (LabelDP). However, existing RR methods primarily focus on the response labels while overlooking the influence of covariates and often do not fully address optimality. To addr…
The remarkable empirical performance of distributional reinforcement learning~(RL) has garnered increasing attention to understanding its theoretical advantages over classical RL. By decomposing the categorical distributional loss commonly employed in distributional RL, we find that the potential su…
Visual odometry needs to be robust against various environmental changes. Although Deep learning (DL) based methods can bring more robust features to visual odometry (VO) than traditional methods, the gap between training and test dataset restricts the performance of DL-based methods when encounteri
Ground point cloud extraction is crucial for route planning of autonomous vehicles in unstructured environments. However, mainstream point cloud extraction methods are susceptible to inaccuracies due to the indistinct obstacle-ground boundary. Furthermore, addressing uneven feature distribution usua
Whether autonomous driving can effectively handle challenging scenarios such as bad weather and complex traffic environments is still in doubt. One of the critical difficulties is that the single-view perception makes it hard to obtain the complementary perceptual information around the multi-condit…
Conditional Stochastic Optimization (CSO) is a powerful modelling paradigm for optimization under uncertainty. The existing literature on CSO is mainly based on the independence assumption of data, which shows that the solution of CSO is asymptotically consistent and enjoys a finite sample guarantee…
Sample average approximation (SAA), a popular method for tractably solving stochastic optimization problems, enjoys strong asymptotic performance guarantees in settings with independent training samples. However, these guarantees are not known to hold generally with dependent samples, such as in onl…
Anderson mixing has been heuristically applied to reinforcement learning (RL) algorithms for accelerating convergence and improving the sampling efficiency of deep RL. Despite its heuristic improvement of convergence, a rigorous mathematical justification for the benefits of Anderson mixing in RL ha…