The ability of existing headway distributions to accurately reflect the diverse behaviors and characteristics in heterogeneous traffic (different types of vehicles) and mixed traffic (human-driven vehicles with autonomous vehicles) is limited, leading to unsatisfactory goodness of fit. To address these issues, we modified the exponential function to obtain a novel headway distribution. Rather than employing Euler's number (e) as the base of the exponential function, we utilized a real number base to provide greater flexibility in modeling the observed headway. However, the proposed is not a probability function. We normalize it to calculate the probability and derive the closed-form equation. In this study, we utilized a comprehensive experiment with five open datasets: highD, exiD, NGSIM, Waymo, and Lyft to evaluate the performance of the proposed distribution and compared its performance with six existing distributions under mixed and heterogeneous traffic flow. The results revealed that the proposed distribution not only captures the fundamental characteristics of headway distribution but also provides physically meaningful parameters that describe the distribution shape of observed headways. Under heterogeneous flow on highways (i.e., uninterrupted traffic flow), the proposed distribution outperforms other candidate distributions. Under urban road conditions (i.e., interrupted traffic flow), including heterogeneous and mixed traffic, the proposed distribution still achieves decent results.

Two new self-driving car services--one in Atlanta from Lyft and May Mobility, another in Las Vegas from Amazon subsidiary Zoox--prove that the robotaxi race is still on. Now comes the hard part. Today, two robotaxi firms operating on opposite sides of the US say they're opening their services to the public. The Ann Arbor tech developer May Mobility has launched its self-driving car service on the Lyft app in a section of Atlanta, Georgia. Starting today, anyone who orders a Lyft in the area might be paired with an autonomous vehicle.

Lyft said it plans to offer driverless vehicles on its platform "as soon as this summer," and that it sees human drivers transitioning to other work such as fleet management as autonomous rides become more ubiquitous. The company has been spending more time pitching its vision for the future of its gig-economy business model as it plays catch-up in offering autonomous rides. Driverless ride-hailing has become more commonplace in some key U.S. markets through competing platforms. Like rival Uber Technologies, Lyft envisions a hybrid future where human drivers will complement autonomous vehicle fleets, especially during periods of peak demand. The autonomous-vehicle economy will create new jobs such as remote vehicle support, fleet management, and map data labeling and validation, said Jeremy Bird, Lyft's executive vice president in charge of driver experience, said Thursday in a blog post.

Lyft is scrambling to compete as Uber racks up autonomous vehicle (AV) partners. On Monday, Lyft said it partnered with Japanese conglomerate Marubeni to bring robotaxis to Dallas roads as soon as next year before expanding to "thousands of vehicles" in other cities. It's the first fruit from Lyft's Mobileye partnership, announced in November. TechCrunch notes that the Intel-owned Mobileye's tech is already available in models from (among others) Audi, Ford, GM, Nissan and Volkswagen. Lyft hasn't yet said which automaker(s) it's partnering with for the Dallas rollout.

Lyft is partnering with Anthropic to bring the startup's AI tech to its platform. "Anthropic, known for its human-centric approach to AI, will work with Lyft to build smart, safe, and empathetic AI-powered products that put riders and drivers first," the two said in a joint press release. If you're a frequent Lyft rider, you can see the early results of that collaboration when you go through the company's customer care AI assistant, which features integration with Anthrophic's Claude chatbot. According to Lyft, the tool is already helping to resolve thousands of customer issues every day, and has reduced average resolution times by 87 percent. Moving forward, Lyft plans to integrate Anthropic's tech across its business.

Lyft has just announced plans to partner with three companies in the autonomous vehicle (AV) sector and gradually introduce their technology into its network starting in 2025. The three companies are Mobileye, May Mobility and Nexar. Mobileye is a pioneer of self-driving technology and has also developed advanced driver assistance systems (ADAS). Lyft's partnership with Mobileye will allow vehicles already equipped with Mobileye's tech to start transporting passengers to their destinations, integrating them into the Lyft network seamlessly. The technology will be available to both small and large fleets on Lyft.

Lyft beat estimates for fourth-quarter profit on Tuesday and said it would generate positive free cash flow for the first time in 2024, as the ride-share platform reaps the benefits of heavy cost cuts. Company shares surged nearly 60% in extended trading but erased a third of those gains after the CFO corrected a major mistake in the earnings report. Erin Brewer had said that the company would grow by 500 basis points (5%) in 2024, but later said that the real increase would be a factor of 10 lower – 50 basis points (0.5%). In 2023, the stock gained about 36%. Rides to stadiums grew more than 35% last year from 2022, mainly driven by Taylor Swift's Eras Tour, Beyoncé's Renaissance World Tour and sporting events, Lyft said.

To better match drivers to riders in our ridesharing application, we revised Lyft's core matching algorithm. We use a novel online reinforcement learning approach that estimates the future earnings of drivers in real time and use this information to find more efficient matches. This change was the first documented implementation of a ridesharing matching algorithm that can learn and improve in real time. We evaluated the new approach during weeks of switchback experimentation in most Lyft markets, and estimated how it benefited drivers, riders, and the platform. In particular, it enabled our drivers to serve millions of additional riders each year, leading to more than $30 million per year in incremental revenue. Lyft rolled out the algorithm globally in 2021.

The rapid development of 3D object detection systems for self-driving cars has significantly improved accuracy. However, these systems struggle to generalize across diverse driving environments, which can lead to safety-critical failures in detecting traffic participants. To address this, we propose a method that utilizes unlabeled repeated traversals of multiple locations to adapt object detectors to new driving environments. By incorporating statistics computed from repeated LiDAR scans, we guide the adaptation process effectively. Our approach enhances LiDAR-based detection models using spatial quantized historical features and introduces a lightweight regression head to leverage the statistics for feature regularization. Additionally, we leverage the statistics for a novel self-training process to stabilize the training. The framework is detector model-agnostic and experiments on real-world datasets demonstrate significant improvements, achieving up to a 20-point performance gain, especially in detecting pedestrians and distant objects. Code is available at https://github.com/zhangtravis/Hist-DA.

There was national news coverage this week of driverless cars pulling off to the side of the road in San Francisco due to fog. This wouldn't have been newsworthy if it didn't involve driverless cars--a technology that is still confounding drivers, policymakers, and passengers. At least compared to the average driver, who can get distracted, tired, drunk, speed, or even just make a judgment error--yes. According to the National Highway Traffic Safety Commission, carmakers have submitted a total of 419 autonomous vehicle crash reports since the advent of the technology. That number isn't insignificant, but consider that according to Berkeley's Transportation Injury Mapping System, in San Francisco there were 3,247 car accidents that resulted in injuries, an average of 270 injuries per month, in just 2022 alone.