-- Urban driving with connected and automated vehicles (CA Vs) offers potential for energy savings, yet most eco-driving strategies focus solely on longitudinal speed control within a single lane. T o address this gap, we propose a novel energy-aware motion planning framework that jointly optimizes longitudinal speed and lateral lane-change decisions using vehicle-to-infrastructure (V2I) communication. Our approach estimates long-term energy costs using a graph-based approximation and solves short-horizon optimal control problems under traffic constraints. Using a data-driven energy model calibrated to an actual battery electric vehicle, we demonstrate with vehicle-in-the-loop experiments that our method reduces motion energy consumption by up to 24% compared to a human driver, highlighting the potential of connectivity-enabled planning for sustainable urban autonomy. Connected and Automated V ehicles (CA Vs) provide benefits in road safety, traffic efficiency, and energy efficiency [1]. Using vehicle-to-infrastructure (V2I) and vehicle-to-vehicle (V2V) communications, CA Vs can coordinate with traffic signals and neighboring vehicles to optimize their motion in ways human drivers are incapable of [2]. Prior studies have shown that by optimizing longitudinal behavior using Signal Phase and Timing (SPaT) data from connected traffic lights, a single CA V can adjust its cruising speed to avoid unnecessary stops, yielding substantial energy savings (11.35 % to 16.4%) [3], [4].

-- The efficient planning of stacking boxes, especially in the online setting where the sequence of item arrivals is unpredictable, remains a critical challenge in modern warehouse and logistics management. Existing solutions often address box size variations, but overlook their intrinsic and physical properties, such as density and rigidity, which are crucial for real-world applications. We use reinforcement learning (RL) to solve this problem by employing action space masking to direct the RL policy toward valid actions. Unlike previous methods that rely on heuristic stability assessments which are difficult to assess in physical scenarios, our framework utilizes online learning to dynamically train the action space mask, eliminating the need for manual heuristic design. Extensive experiments demonstrate that our proposed method outperforms existing state-of-the-arts. Furthermore, we deploy our learned task planner in a real-world robotic palletizer, validating its practical applicability in operational settings. I. INTRODUCTION In modern warehouse and logistics management, stacking boxes continues to be a common challenge. In the past, due to the smaller scale of trade and lower efficiency requirements, workers could rely on their experience to decide how each box should be placed. However, with the globalization of trade, there is a growing need for fast and stable box stacking, and a good solution for this is robotic palletization [1] [2].

Single occupancy vehicles are the most attractive transportation alternative for many commuters, leading to increased traffic congestion and air pollution. Advancements in information technologies create opportunities for smart solutions that incentivize ridesharing and mode shift to higher occupancy vehicles (HOVs) to achieve the car lighter vision of cities. In this study, we present HumanLight, a novel decentralized adaptive traffic signal control algorithm designed to optimize people throughput at intersections. Our proposed controller is founded on reinforcement learning with the reward function embedding the transportation-inspired concept of pressure at the person-level. By rewarding HOV commuters with travel time savings for their efforts to merge into a single ride, HumanLight achieves equitable allocation of green times. Apart from adopting FRAP, a state-of-the-art (SOTA) base model, HumanLight introduces the concept of active vehicles, loosely defined as vehicles in proximity to the intersection within the action interval window. The proposed algorithm showcases significant headroom and scalability in different network configurations considering multimodal vehicle splits at various scenarios of HOV adoption. Improvements in person delays and queues range from 15% to over 55% compared to vehicle-level SOTA controllers. We quantify the impact of incorporating active vehicles in the formulation of our RL model for different network structures. HumanLight also enables regulation of the aggressiveness of the HOV prioritization. The impact of parameter setting on the generated phase profile is investigated as a key component of acyclic signal controllers affecting pedestrian waiting times. HumanLight's scalable, decentralized design can reshape the resolution of traffic management to be more human-centric and empower policies that incentivize ridesharing and public transit systems.

Cooperation-A ware Lane Change Control in Dense Traffic Sangjae Bae 1, Dhruv Saxena 2, Alireza Nakhaei 3, Chiho Choi 3, Kikuo Fujimura 3, and Scott Moura 1 Abstract -- This paper presents a real-time lane change control framework of autonomous driving in dense traffic, which exploits cooperative behaviors of human drivers. This paper especially focuses on heavy traffic where vehicles cannot change lane without cooperating with other drivers. In this case, classical robust controls may not apply since there is no "safe" area to merge to. That said, modeling complex and interactive human behaviors is nontrivial from the perspective of control engineers. We propose a mathematical control framework based on Model Predictive Control (MPC) encompassing a state-of-the-art Recurrent Neural network (RNN) architecture. In particular, RNN predicts interactive motions of human drivers in response to potential actions of the autonomous vehicle, which are then be systematically evaluated in safety constraints. We also propose a real-time heuristic algorithm to find locally optimal control inputs. Finally, quantitative and qualitative analysis on simulation studies are presented, showing a strong potential of the proposed framework. I NTRODUCTION An autonomous-driving vehicle is no longer a futuristic concept and extensive researches have been conducted in various aspects, spanning from localization, perceptions, and controls to implementations and validations. Particularly from the perspective of control engineers, designing a controller that secures safety, in various traffic conditions, such as driving on arterial-road/highway in free-flow/dense traffic with/without traffic lights, has been a principal research focus.