Dawei Wang

Dawei Wang, Lingping Gao, Ziquan Lan et al.

Recently, there have been many advances in autonomous driving society, attracting a lot of attention from academia and industry. However, existing works mainly focus on cars, extra development is still required for self-driving truck algorithms and models. In this paper, we introduce an intelligent self-driving truck system. Our presented system consists of three main components, 1) a realistic traffic simulation module for generating realistic traffic flow in testing scenarios, 2) a high-fidelity truck model which is designed and evaluated for mimicking real truck response in real-world deployment, 3) an intelligent planning module with learning-based decision making algorithm and multi-mode trajectory planner, taking into account the truck's constraints, road slope changes, and the surrounding traffic flow. We provide quantitative evaluations for each component individually to demonstrate the fidelity and performance of each part. We also deploy our proposed system on a real truck and conduct real world experiments which shows our system's capacity of mitigating sim-to-real gap. Our code is available at https://github.com/InceptioResearch/IITS

Dawei Wang, Yunhui He, Hans De Sterck

Empirical results show that Anderson acceleration (AA) can be a powerful mechanism to improve the asymptotic linear convergence speed of the Alternating Direction Method of Multipliers (ADMM) when ADMM by itself converges linearly. However, theoretical results to quantify this improvement do not exist yet. In this paper we explain and quantify this improvement in linear asymptotic convergence speed for the special case of a stationary version of AA applied to ADMM. We do so by considering the spectral properties of the Jacobians of ADMM and the stationary version of AA evaluated at the fixed point, where the coefficients of the stationary AA method are computed such that its asymptotic linear convergence factor is optimal. The optimal linear convergence factors of this stationary AA-ADMM method are computed analytically or by optimization, based on previous work on optimal stationary AA acceleration. Using this spectral picture and those analytical results, our approach provides new insight into how and by how much the stationary AA method can improve the asymptotic linear convergence factor of ADMM. Numerical results also indicate that the optimal linear convergence factor of the stationary AA methods gives a useful estimate for the asymptotic linear convergence speed of the non-stationary AA method that is used in practice.