Rising labor costs and increasing logistical demands pose significant challenges to modern delivery systems. Automated Electric Vehicles (AEVs) could reduce reliance on delivery personnel and increase route flexibility, but their adoption is limited due to varying customer acceptance and integration complexities. Shared Distribution Locations (SDLs) offer an alternative to door-to-door (D2D) delivery by providing a wider delivery window and serving multiple community customers, thereby improving last-mile logistics through reduced delivery time, lower costs, and higher customer satisfaction.This paper introduces the Multi-Trip Time-Dependent Hybrid Vehicle Routing Problem (MTTD-MVRP), a challenging variant of the Vehicle Routing Problem (VRP) that combines Autonomous Electric Vehicles (AEVs) with conventional vehicles. The problem's complexity arises from factors such as time-dependent travel speeds, strict time windows, battery limitations, and driver labor constraints, while integrating both SDLs and D2D deliveries. To solve the MTTD-MVRP efficiently, we develop a tailored meta-heuristic based on Adaptive Large Neighborhood Search (ALNS) augmented with column generation (CG). This approach intensively explores the solution space using problem-specific operators and adaptively refines solutions, balancing high-quality outcomes with computational effort. Extensive experiments show that the proposed method delivers near-optimal solutions for large-scale instances within practical time limits.From a managerial perspective, our findings highlight the importance of integrating autonomous and human-driven vehicles in last-mile logistics. Decision-makers can leverage SDLs to reduce operational costs and carbon footprints while still accommodating customers who require or prefer D2D services.

In recent years, the development of autonomous electric vehicles (AEVs) has gained significant attention from researchers and engineers worldwide. AEVs are expected to revolutionize the way we commute and transport goods, offering safer and more efficient solutions to our transportation needs.

Advanced driver assistance systems (ADAS) hold immense promise. At times, the headlines about the autonomous vehicle (AV) industry seem ominous, with a focus on accidents, regulation or company valuations that some find undeserving. None of this is unreasonable, but it makes the amazing possibilities of a world of AVs seem opaque. One of the universally accepted upsides of AVs is the potential positive impact on the environment, as most AVs will also be electric vehicles (EVs). Industry analyst reports project that by 2023, 7.3 million vehicles (7% of the total market) will have autonomous driving capabilities requiring $1.5 billion of autonomous-driving-dedicated processors.

Knowledge transfer is a promising concept to achieve real-time decision-making for autonomous vehicles. This paper constructs a transfer deep reinforcement learning framework to transform the driving tasks in inter-section environments. The driving missions at the un-signalized intersection are cast into a left turn, right turn, and running straight for automated vehicles. The goal of the autonomous ego vehicle (AEV) is to drive through the intersection situation efficiently and safely. This objective promotes the studied vehicle to increase its speed and avoid crashing other vehicles. The decision-making pol-icy learned from one driving task is transferred and evaluated in another driving mission. Simulation results reveal that the decision-making strategies related to similar tasks are transferable. It indicates that the presented control framework could reduce the time consumption and realize online implementation.

Decision-making strategy for autonomous vehicles de-scribes a sequence of driving maneuvers to achieve a certain navigational mission. This paper utilizes the deep reinforcement learning (DRL) method to address the continuous-horizon decision-making problem on the highway. First, the vehicle kinematics and driving scenario on the freeway are introduced. The running objective of the ego automated vehicle is to execute an efficient and smooth policy without collision. Then, the particular algorithm named proximal policy optimization (PPO)-enhanced DRL is illustrated. To overcome the challenges in tardy training efficiency and sample inefficiency, this applied algorithm could realize high learning efficiency and excellent control performance. Finally, the PPO-DRL-based decision-making strategy is estimated from multiple perspectives, including the optimality, learning efficiency, and adaptability. Its potential for online application is discussed by applying it to similar driving scenarios.

Increase in greenhouse gases emission from the transportation sector has led companies and government to elevate and support the production of electric vehicles. The natural synergy between increased support for electric and emergence of autonomous vehicles possibly can relieve the limitations regarding access to charging infrastructure, time management, and range anxiety. In this work, a fleet of Autonomous Electric Vehicles (AEV) is considered for transportation and logistic capabilities with limited battery capacity and scarce charging station availability are considered while planning to avoid inefficient routing strategies. We introduce a min-max autonomous electric vehicle routing problem (AEVRP) where the maximum distance traveled by any AEV is minimized while considering charging stations for recharging. We propose a genetic algorithm based meta-heuristic that can efficiently solve a variety of instances. Extensive computational results, sensitivity analysis, and data-driven simulation implemented with the robot operating system (ROS) middleware are performed to corroborate the efficiency of the proposed approach, both quantitatively and qualitatively.