AutopilotingConnected and Autonomous Vehicles(CAVs) is an important application for mobile computing. A promising context to realize autopiloting CAVs is cooperative driving on dedicated highways. For such a context, an indispensable driving scenario isCooperative Lane Change(CLC). Due to the safety concerns of this driving scenario, a verifiably safe solution is needed (at least, the solution design should be formally provably safe). However, this demand is complicated by the inherently unreliable wireless communications between the CAVs. In this paper, we focus on the well-adoptedConstant Time Headway(CTH) safety rule. We propose a CLC protocol, and formally prove its guarantee of the CTH safety and liveness, even under arbitrary wireless packet losses. These theoretical claims are further confirmed by our simulations. The simulation results also show that our proposed protocol significantly improves lane change success rates (by$5.3\% \sim +\infty \%$) than other alternatives under adverse conditions. Furthermore, the sensitivity study results also show our protocol can tolerate reasonable disturbances.

Object segmentation is a per-pixel label prediction task that targets at providing context analysis for autonomous driving. Moving-object segmentation (MOS) serves as a subbranch of object segmentation, targeting to separating the surrounding objects into binary options: dynamic and static. MOS is vital for the safety-critical task in autonomous driving because dynamic objects are often a true potential threat to self-driving cars compared to static ones. Current methods typically address the MOS problem as a category feature to label the mapping task, which is not rational in reality. For example, a parking car should be considered as static instead of a moving-object category. There is a little systematic theory to differentiate object moving characteristics from nonmoving characteristics in MOS. Furthermore, restricted by limited resources in the embedded system, MOS is often in an offline manner due to huge computational requirements. An online and low computational cost MOS is an urgent demand for the practical safety-critical mission which takes immediate reaction as compulsory. In this article, we propose EmPointMovSeg, an efficient and practical 3-D LiDAR MOS solution for autonomous driving. Leveraging the power of the well-adapted autoregressive system identification (AR-SI) theory, EmPointMovSeg theoretically explains the moving-object feature in large-scale 3-D LiDAR semantic segmentation. An end-to-end sparse tensor-based CNN which balances segmentation accuracy and online process ability is proposed. We construct our experiment on both representative dataset benchmarks and practical embedded systems. The evaluation result shows the effectiveness and accuracy of our proposed solution, conquering the bottleneck in the online large-scale 3-D LiDAR semantic segmentation.

To realize the grand vision of automated driving in smart vehicle cyber-physical systems (CPS), one important task is to support the merging of connected automated vehicles (CAVs) from a metered-ramp to highway. Certain safety rules must be guaranteed. However, this demand is complicated by the inherently unreliable wireless communications. In this article, we focus on the well adopted constant-time-headway (CTH) safety rule. We propose a highway and metered-ramp CAV collaborative merging protocol, and formally prove its guarantee of the CTH safety and liveness under arbitrary wireless data packet losses . These theoretical claims are further validated by our simulations. Furthermore, the simulation results also show significant improvements in the merging efficiency over other solution alternatives. Particularly, the merging success rates are more than 99% better in 11 out of 18 comparison pairs, and 0% (i.e., tied) ∼ 71% better in the remaining 7 comparison pairs.