Autonomous Trucks Market Size, Share & Analysis [2026-2035]. The autonomous trucks market is projected to grow from USD 50.82 billion in 2026 to USD 158.69 billion by 2035, at a CAGR of 13.5%. Market growth is being driven by the expansion of autonomous freight operations across hub-to-hub corridors, logistics hubs, ports, and distribution networks, where predictable routes and high vehicle utilization support autonomous deployment. Daimler Truck, Volvo Group, TRATON Group, PACCAR, and Hyundai Motor Company are collaborating with autonomous driving technology providers, including Aurora Innovation, Plus, Torc Robotics, Waabi, and Kodiak Robotics, for commercial deployments of Level 4 trucks for long-haul freight transportation, while also advancing Level 2 and Level 3 automated driving capabilities in production vehicles. At the same time, investments in sensor redundancy, remote fleet supervision, AI-based perception systems, and validation programs are supporting the transition from controlled pilots to revenue-generating freight operations. These developments are accelerating the adoption of autonomous trucks across commercial logistics and freight transportation networks. By application, the last-mile delivery trucks segment is expected to account for the largest share of the autonomous trucks market during the forecast period. The segment is supported by the increasing use of autonomous trucks for scheduled freight movement between distribution centers, warehouses, and retail locations, where fixed routes and recurring operations simplify deployment. Companies are prioritizing these use cases to scale autonomous transportation in commercial logistics. In November 2025, Daimler Truck expanded the deployment of Active Brake Assist and Active Sideguard Assist across selected Mercedes-Benz Actros models in Europe. These systems provide Automatic Emergency Braking (AEB) and Blind Spot Detection (BSD) capabilities, enhancing collision avoidance and vulnerable road user protection in commercial freight operations. The development highlights the growing adoption of advanced driver assistance technologies to improve safety and support the progression toward higher levels of vehicle automation. As commercial adoption expands across retail and consumer goods distribution, last-mile delivery trucks are expected to remain the largest application segment in the autonomous trucks market. By level of autonomy, the L4 (level 4) segment is expected to witness the fastest growth during the forecast period. Growth in this segment is driven by increasing investments in autonomous highway freight solutions and the shift from testing to planned commercial deployment. Truck manufacturers are integrating L4 technology into production platforms to support hub-to-hub transportation, where defined operating conditions enable autonomous driving with minimal human intervention. In January 2026, TRATON Group (Germany) and PlusAI (US) expanded their global partnership to accelerate the development and commercialization of L4 autonomous trucks for highway operations in the US and Europe. The collaboration includes factory integration of autonomous driving technology across TRATON's truck brands and supports future large-scale deployment. Such initiatives are expected to accelerate the adoption of L4 autonomous trucks and drive the segment's growth during the forecast period. By sensor type, the LiDAR segment is expected to account for the largest share of the autonomous trucks market during the forecast period. LiDAR is widely used in commercial vehicles to support object detection, environmental mapping, lane positioning, and obstacle recognition. Its adoption is increasing across Level 2, Level 3, and Level 4 truck platforms as OEMs focus on improving perception capabilities and driving performance. For example, in May 2026, Hesai Technology (China) secured a design win from a leading European OEM, resulting in an order for more than one million LiDAR units. The development highlights the growing integration of LiDAR into next-generation vehicle platforms equipped with advanced driver assistance and automated driving functions. Additionally, in September 2025, the Stark Area Regional Transit Authority (SARTA) showcased buses equipped with Perrone Robotics' TONY/MAX platform, integrating LiDAR, lane keeping, and automated braking technologies. These deployments demonstrate the expanding use of LiDAR-enabled semi-autonomous and autonomous driving systems in commercial and public transportation vehicles. Europe is expected to be the fastest-growing market for autonomous trucks during the forecast period. The region is witnessing increased investment in automated freight transportation, connected commercial vehicles, and autonomous public mobility programs. Growth is also supported by active participation from truck OEMs, bus manufacturers, autonomous driving technology developers, and logistics operators. For example, in January 2026, IVECO Group (Italy) and Plus (US) launched a new autonomous driving program in Spain to develop and validate autonomous heavy-duty trucks for commercial operations. In June 2026, a fully autonomous bus line was introduced in Barcelona, Spain, as part of a public mobility initiative to demonstrate autonomous transit operations in an urban environment. Such deployments highlight the region's efforts to expand autonomous driving technologies across both freight and passenger transportation applications. As autonomous truck and bus programs continue to scale across Europe, the region is expected to register the highest growth in the autonomous trucks market during the forecast period. About MarketsandMarkets. MarketsandMarkets(TM) provides quantified B2B research on 30,000 high growth emerging opportunities/threats which will impact 70% to 80% of worldwide companies' revenues. Currently servicing 7500 customers worldwide including 80% of global Fortune 1000 companies as clients. Almost 75,000 top officers across eight industries worldwide approach MarketsandMarkets(TM) for their pain-points around revenues decisions. * Automotive LiDAR Market Size, Share & Trends [2026-2033] 07/14/2026 · Comments Off on Automotive LiDAR Market Size, Share & Trends [2026-2033] * Autonomous Vehicle Market Size, Share, Trends & Forecast 2035 09/26/2025 · Comments Off on Autonomous Vehicle Market Size, Share, Trends & Forecast 2035 * Autonomous Trucks Market Size, Share, Trends & Analysis by 2030 08/29/2025 · Comments Off on Autonomous Trucks Market Size, Share, Trends & Analysis by 2030

Kodiak advances autonomous driving safety validation with two new AI tools. By Zahra Awan June 8, 2026 4 Mins Read Rapid AI advances now enable engineers to develop autonomous driving technology faster than ever, but the true frontier of autonomous driving is the ability to couple those advances with demonstrable and rigorous safety. Progress in autonomous vehicle development no longer depends on larger budgets or fleets, but on extracting meaningful insights from real-world testing and simulation. The focus is on validating how autonomous systems respond to rare and complex scenarios that may occur only infrequently in real-world driving. To meet this challenge, Kodiak says it has adopted two tools that accelerate the pace, depth and precision of safety engineering to deliver clear, compelling evidence of the safety of Kodiak Driver. The first tool is Kodiak's Probabilistic Risk Assessment (PRA), a methodology that the company uses to estimate the expected rate of collisions of varying severities for the Kodiak Driver and to identify the key scenarios, risk factors and autonomy failure modes that dominate the risk profile. The company then compares this output against human performance baselines, which are established in partnership with leading centers of transportation research. The second tool is BreakPoint, an artificial intelligence (AI) validation tool that the company says hunts with intelligence and efficiency for edge cases that could result in collisions or other undesirable behavior. The analysis provided by BreakPoint helps inform the company's PRA models, enabling Kodiak to identify key risk areas for the Kodiak Driver and prioritize development efforts accordingly. Together, these tools form part of the company's safety case and support the development and deployment of its AI-powered autonomous driving system. Quantifying safety. The Kodiak PRA combines Bayesian probability theory, systems engineering, reliability analysis and statistical modeling to quantify safety performance. It is designed to estimate collision rates for rare scenarios that are difficult to capture through real-world testing alone, while also quantifying the uncertainty associated with those estimates. This enables the company to identify where evidence is strong and where additional validation is needed. The Kodiak PRA breaks down operating scenarios into three key factors: scenario exposure, which measures how often the vehicle encounters a particular situation; collision likelihood, which estimates the probability of a collision occurring when that scenario is encountered; and collision severity, which assesses the potential consequences of a collision in that scenario. The PRA provides a quantitative framework for assessing known hazardous scenarios and evaluating their associated risks. BreakPoint complements this by identifying previously unknown or untested hazardous scenarios, bringing them into the scope of analysis. Together, the tools support a systematic approach to identifying, quantifying and mitigating safety risks, helping to address the challenges outlined in the Safety of the Intended Functionality (SOTIF) framework. Risk-informed prioritization. With the PRA results, Kodiak directly compares the Kodiak Driver against the rate of collisions from comparable human drivers in analogous operating environments. By breaking down driving into structured events ranging from the mundane to rare, the company says it can estimate risk with bounded uncertainty and ensure the Kodiak Driver achieves superior performance. Beyond deployment decisions, the PRA helps identify and prioritize key risk factors throughout development. Its Bayesian framework highlights where additional evidence is needed, helping to focus testing and validation on less-explored scenarios. BreakPoint. A common engineering testing technique is to deliberately inject faults into a system and observe the system's response, to ensure it can handle the fault as expected. BreakPoint introduces realistic, time-varying errors into the autonomy system to evaluate performance under normal and challenging conditions. The tool uses an adversarial approach to identify potential failure modes by searching for scenarios that could lead to collisions. These findings are then assessed for risk and incorporated into the PRA framework to support ongoing safety analysis. According to Kodiak, BreakPoint has identified previously unknown failure modes that had not been observed during real-world testing. One example involved a low-probability scenario in which the perception system could incorrectly estimate the speed of stalled vehicles within its industrial operating domain. The company said identifying the issue in simulation enabled it to address the scenario more quickly, noting that it may have required many thousands of miles of real-world driving to encounter the same event.