The implementation of autonomous delivery solutions in last-mile logistics operations is considered promising. Autonomous delivery solutions can help in tackling urban challenges related to last-mile logistics operations. Urbanization creates higher mobility and transportation demand, which contributes to increased congestion levels, traffic, air pollution, and accident rates. Moreover, mega-trends, such as e-commerce, demand that logistics companies react to increased customer expectations in terms of delivery time and service. Concerning service, electrified autonomous delivery solutions have the potential to operate 24/7 and can help to overcome driver shortages. This paper conducts a systematic literature review. Based on the literature set, a snowballing procedure was applied. Complementary gray literature was included. This work discusses different autonomous delivery solutions such as Autonomous Delivery Robots (ADRs), Unmanned Aerial Vehicles (UAVs), two- or multi-tiered systems, and the concept of passenger and freight integration. The work presents advantages and disadvantages, enabling the comparison of solutions. Furthermore, a research agenda is provided, from which practical-managerial and theoretical implications can be derived. The research agenda can help researchers, manufacturers, businesses, and governmental institutions to prepare for the arrival and subsequent implementation of autonomous delivery services. Various implications related to energy demand, legislation, implementation strategy, training, and risk and safety are presented. The outcome of this work calls for collaboration among various stakeholders, encourages mutual learning, and hints at the importance of national and international development projects.

Current technological developments allow the testing of shared autonomous electric vehicles in real-life conditions. Consequently, we can evaluate how users react and if these developments might lead to more sustainable transport behaviour in the future. The purpose of this study was to capture public opinion regarding autonomous vehicles in terms of user experience and intended future use. Autonomous shuttles were operated in the Brussels Capital Region in a mixed traffic situation, allowing interaction with other road users. We compared the results of two pilots with different target groups in the same city. Public opinion was captured through an online survey after passengers had experienced a ride. Our results showed that more than 70% of the passengers expressed no concerns with regards to autonomous vehicles. The majority had a positive experience with the shuttle and evaluated the shuttle positively in terms of driving behaviour, entry and exit and comfort. The regression analysis indicated that enjoyment of the ride is an important factor that contributes to the intention to use autonomous shuttles in the future.

Autonomous vehicles have the potential to disrupt the mobility system. Therefore, it is important to understand attitude formation towards autonomous vehicles. The focus of this study is on the private user’s technology acceptance of an autonomous vehicle. The study applies the determinants of technology acceptance to capture users’ attitude towards and intention to adopt autonomous vehicles. A field experiment with 27 participants was conducted to assess changes in determinants before and after a test ride with a level 2 automated vehicle. The automated vehicle was equipped with technology that allowed a hands-off, feet-off experience on a public road in real traffic. The results show that a ride has a positive and significant effect on attitudes towards autonomous vehicles. Additionally, participants with higher ratings of technology anxiety show a remarkable increase in attitude towards autonomous vehicles after the ride compared to participants with lower levels of technology anxiety. These findings indicate that experience with a partially automated vehicle has a potentially positive effect on the acceptance of autonomous vehicles. As such, our study illustrates the importance of continuous pilot testing with private automated vehicles to increase future user acceptance of autonomous vehicles.

Signalized intersections can increase the vehicle stops and consequently increase the energy consumption by forcing stop-and-go dynamics on vehicles. Eco-driving with the help of connectivity is a solution that could avoid multiple stops and improve energy efficiency. In this paper, an eco-driving framework is developed, which finds the energy-efficient speed profile both up- and downstream of a signalized intersection in free-flow situations (eco-FF). The proposed framework utilizes the signal phasing and timing (SPaT) data that are communicated to the vehicle. The energy consumption model used in this framework is a combination of vehicle dynamics and time-dependent auxiliary consumption, which implicitly incorporates the travel time into the function and is validated with real-world test data. It is shown that, by using the proposed eco-FF framework, the vehicle’s energy consumption is notably reduced.

As autonomous vehicle technology advances, the development of energy-efficient control methodologies emerges as a critical area in the literature. This includes the behavior control of vehicles near signalized intersections, which still needs comprehensive exploration. Through connectivity, the adoption of promising eco-driving approaches can manage a vehicle’s speed profile to improve energy consumption. This study focuses on controlling the speed of an autonomous electric vehicle (AEV) both up and downstream of a signalized intersection in the presence of preceding vehicles. In order to achieve this, a dynamic pro-active predictive cruise control eco-driving (eco-PPCC) framework is developed that, instead of merely reacting to the preceding vehicle’s speed changes, uses the preceding vehicle’s upcoming data to actively adjust and optimize the speed profile of the AEV. The proposed algorithm is compared to the conventional Gipps and eco-PCC models for benchmarking and performance analysis through numerous scenarios. Additionally, real-world measurements are performed and taken to consider practical use cases. The results demonstrate that when compared to the two baseline methods, the proposed framework can add significant value to reducing energy consumption, preventing unnecessary stops at intersections, and improving travel time.