An advanced autonomous logistics simulation is developed where multiple smart delivery trucks operate within a dynamic city-wide road network. Each truck acts as an agent capable of bidding on delivery orders, planning optimal routes, managing battery levels, and seeking charging stations, all while aiming to maximize profit through self-interested decision-making. The simulation demonstrates how agentic behaviors emerge from simple rules, how competition influences order allocation, and how a graph-based world facilitates realistic movement, routing, and resource constraints. The simulation's core components include defining the AgenticTruck class, initializing key attributes like position, battery, balance, and state, and implementing decision-making logic for tasks such as calculating shortest paths, identifying charging stations, and evaluating order profitability. Trucks are designed to transition smoothly between states like moving, charging, and idling, while managing battery recharging, financial impacts of movement, fuel consumption, and order completion. The simulation orchestrates agent interactions by generating a graph-based city, spawning trucks with varying capacities, and producing new delivery orders, with agents bidding for tasks based on profitability and distance. The simulation loop updates agent states, visualizes the network, displays active orders, and animates each truck’s movement, showcasing emergent coordination and competition within the multi-agent logistics ecosystem. This setup allows for observing dynamics that mirror real-world fleet behavior, providing a sandbox for experimenting with logistics intelligence. The project highlights the potential of autonomous systems in logistics, demonstrating how individual components like graph generation, routing, battery management, auctions, and visualization can form a cohesive, evolving system. This matters because it showcases the potential of AI and autonomous systems in transforming logistics and supply chain management, offering insights into optimizing efficiency and resource allocation.
Kodiak AI is collaborating with Bosch to develop a system that equips standard big rigs with autonomous driving capabilities, aiming to scale its self-driving truck technology. Announced at the 2026 Consumer Electronics Show, this partnership will integrate Bosch's hardware components, such as sensors and steering technologies, into Kodiak's redundant self-driving systems. This collaboration allows for the conversion of semi trucks into autonomous vehicles either during production or through third-party upfitting, enhancing the modularity and serviceability of the technology. While Kodiak has already deployed driverless trucks for commercial operations, the timeline for broader availability of these systems remains unspecified. This matters because advancing autonomous truck technology could significantly impact logistics and transportation industries by improving efficiency and safety.